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        <h1 class="post-title">Publications</h1>
        <p class="post-description"></p>
      </header>
      <article>
        <table>
          <tbody>
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              <td>
                [<a href="#" target="_blank" rel="noopener noreferrer"
                  >Google scholar</a
                >]
              </td>
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                [<a href="#" target="_blank" rel="noopener noreferrer">DBLP</a>]
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              <td>
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                  >View by topic</a
                >]
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        <h4 id="preprints">Preprints</h4>
        <ul>
          <li>
            PromptBench: Towards Evaluating the Robustness of Large Language
            Models on Adversarial Prompts. Kaijie Zhu, Jindong Wang, Jiaheng
            Zhou, Zichen Wang, Hao Chen, Yidong Wang, Linyi Yang, Wei Ye, Neil
            Zhenqiang Gong, Yue Zhang, Xing Xie. [<a
              href="#"
              target="_blank"
              rel="noopener noreferrer"
              >arxiv</a
            >] [<a href="#" target="_blank" rel="noopener noreferrer">code</a>]
          </li>
          <li>
            PandaLM: An Automatic Evaluation Benchmark for LLM Instruction
            Tuning Optimization. Yidong Wang, Zhuohao Yu, Zhengran Zeng, Linyi
            Yang, Cunxiang Wang, Hao Chen, Chaoya Jiang, Rui Xie, Jindong Wang,
            Xing Xie, Wei Ye, Shikun Zhang, Yue Zhang. [<a
              href="#"
              target="_blank"
              rel="noopener noreferrer"
              >arxiv</a
            >] [<a href="#" target="_blank" rel="noopener noreferrer">code</a>]
          </li>
          <li>
            Selective Mixup Helps with Distribution Shifts, But Not (Only)
            because of Mixup. Damien Teney, Jindong Wang, Ehsan Abbasnejad. [<a
              href="#"
              target="_blank"
              rel="noopener noreferrer"
              >arxiv</a
            >]
          </li>
          <li>
            Imprecise Label Learning: A Unified Framework for Learning with
            Various Imprecise Label Configurations. Hao Chen, Ankit Shah,
            Jindong Wang, Ran Tao, Yidong Wang, Xing Xie, Masashi Sugiyama, Rita
            Singh, Bhiksha Raj. [<a
              href="#"
              target="_blank"
              rel="noopener noreferrer"
              >arxiv</a
            >]
          </li>
          <li>
            Exploring Vision-Language Models for Imbalanced Learning. Yidong
            Wang, Zhuohao Yu, <strong>Jindong Wang</strong>, Qiang Heng, Hao
            Chen, Wei Ye, Rui Xie, Xing Xie, Shikun Zhang. [<a
              href="#"
              target="_blank"
              rel="noopener noreferrer"
              >arxiv</a
            >] [<a href="#" target="_blank" rel="noopener noreferrer">code</a>]
          </li>
          <li>
            An Embarrassingly Simple Baseline for Imbalanced Semi-Supervised
            Learning. Hao Chen, Yue Fan, Yidong Wang,
            <strong>Jindong Wang</strong>, Bernt Schiele, Xing Xie, Marios
            Savvides, Bhiksha Raj. [<a
              href="#"
              target="_blank"
              rel="noopener noreferrer"
              >arxiv</a
            >]
          </li>
          <li>
            FIXED: Frustratingly Easy Domain Generalization with Mixup. Wang Lu,
            <strong>Jindong Wang</strong>, Han Yu, Lei Huang, Xiang Zhang,
            Yiqiang Chen, Xing Xie. [<a
              href="#"
              target="_blank"
              rel="noopener noreferrer"
              >arxiv</a
            >]
          </li>
          <li>
            Conv-Adapter: Exploring Parameter Efficient Transfer Learning for
            ConvNets. Hao Chen, Ran Tao, Han Zhang, Yidong Wang, Wei Ye, Jindong
            Wang, Guosheng Hu, and Marios Savvides. [<a
              href="#"
              target="_blank"
              rel="noopener noreferrer"
              >arxiv</a
            >]
          </li>
          <li>
            Towards Optimization and Model Selection for Domain Generalization:
            A Mixup-guided Solution. Wang Lu, <strong>Jindong Wang</strong>,
            Yidong Wang, Kan Ren, Yiqiang Chen, Xing Xie. [<a
              href="#"
              target="_blank"
              rel="noopener noreferrer"
              >arxiv</a
            >]
          </li>
          <li>
            Equivariant Disentangled Transformation for Domain Generalization
            under Combination Shift. Yivan Zhang, <strong>Jindong Wang</strong>,
            Xing Xie, and Masashi Sugiyama. [<a
              href="#"
              target="_blank"
              rel="noopener noreferrer"
              >arxiv</a
            >]
          </li>
          <li>
            Boosting Cross-Domain Speech Recognition with Self-Supervision. Han
            Zhu, Gaofeng Cheng, <strong>Jindong Wang</strong>, Wenxin Hou,
            Pengyuan Zhang, and Yonghong Yan. [<a
              href="#"
              target="_blank"
              rel="noopener noreferrer"
              >arxiv</a
            >]
          </li>
          <li>
            Learning Invariant Representations across Domains and Tasks.
            <strong>Jindong Wang</strong>, Wenjie Feng, Chang Liu, Chaohui Yu,
            Mingxuan Du, Renjun Xu, Tao Qin, and Tie-Yan Liu. [<a
              href="#"
              target="_blank"
              rel="noopener noreferrer"
              >arxiv</a
            >]
          </li>
          <li>
            Learning to match distributions for domain adaptation. Chaohui Yu,
            <strong>Jindong Wang</strong>, Chang Liu, Tao Qin, Renjun Xu, Wenjie
            Feng, Yiqiang Chen, and Tie-Yan Liu. [<a
              href="#"
              target="_blank"
              rel="noopener noreferrer"
              >arxiv</a
            >]
          </li>
        </ul>
        <h4 id="books">Books</h4>
        <div class="publications">
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    <abbr class="badge">Book</abbr>
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                <div
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                >
                  Introduction to Transfer Learning
                </div>
                <div
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                  "
                >
                  <em><b>Jindong Wang</b></em>
                  , and Yiqiang Chen
                </div>
                <div
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                  "
                >
                  2021 | [
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                    >HTML</a
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                    >Zhihu</a
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        </div>
        <h4 id="papers">Papers</h4>
        <div class="publications">
          <div>2023</div>
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                <div
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                >
                  Generalizable Low-Resource Activity Recognition with Diverse
                  and Discriminative Representation Learning
                </div>
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                  "
                >
                  Xin Qin,
                  <em><b>Jindong Wang</b></em
                  ><sup>#</sup>
                  , Shuo Ma, Wang Lu, Yongchun Zhu, Xing Xie, and Yiqiang Chen
                </div>
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                  "
                >
                  <em
                    >The 29th ACM SIGKDD Conference on Knowledge Discovery and
                    Data Mining (KDD)</em
                  >
                  2023 | [
                  <a
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                    >arXiv</a
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                    >Code</a
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                >
                  Domain-Specific Risk Minimization for Out-of-Distribution
                  Generalization
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                  "
                >
                  Yi-Fan Zhang,
                  <em><b>Jindong Wang</b></em
                  ><sup>#</sup>
                  , Jian Liang, Zhang Zhang, Baosheng Yu, Liang Wang, Dacheng
                  Tao, and Xing Xie
                </div>
                <div
                  style="
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                  "
                >
                  <em
                    >The 29th ACM SIGKDD Conference on Knowledge Discovery and
                    Data Mining (KDD)</em
                  >
                  2023 | [
                  <a
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                >
                  GLUE-X: Evaluating Natural Language Understanding Models from
                  an Out-of-distribution Generalization Perspective
                </div>
                <div
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                >
                  Linyi Yang, Shuibai Zhang, Libo Qin, Yafu Li, Yidong Wang,
                  Hanmeng Liu,
                  <em><b>Jindong Wang</b></em>
                  , Xing Xie, and Yue Zhang
                </div>
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                  "
                >
                  <em
                    >The 61st Annual Meeting of the Association for
                    Computational Linguistics (ACL) Findings</em
                  >
                  2023 | [
                  <a
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                    >arXiv</a
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                >
                  Out-of-distribution Representation Learning for Time Series
                  Classification
                </div>
                <div
                  class="author"
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                  "
                >
                  Wang Lu,
                  <em><b>Jindong Wang</b></em
                  ><sup>#</sup>
                  , Xinwei Sun, Yiqiang Chen, and Xing Xie
                </div>
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                  style="
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                  "
                >
                  <em
                    >International Conference on Learning Representations
                    (ICLR)</em
                  >
                  2023 | [
                  <a
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                    >arXiv</a
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                    >Zhihu</a
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                >
                  FreeMatch: Self-adaptive Thresholding for Semi-supervised
                  Learning
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                >
                  Yidong Wang, Hao Chen, Qiang Heng, Wenxin Hou, Yue Fan, Zhen
                  Wu,
                  <em><b>Jindong Wang</b></em
                  ><sup>#</sup>
                  , Marios Savvides, Takahiro Shinozaki, Bhiksha Raj, Bernt
                  Schiele, and Xing Xie
                </div>
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                  style="
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                  "
                >
                  <em
                    >International Conference on Learning Representations
                    (ICLR)</em
                  >
                  2023 | [
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                    >arXiv</a
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                    >Zhihu</a
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                <div
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                >
                  SoftMatch: Addressing the Quantity-Quality Tradeoff in
                  Semi-supervised Learning
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                >
                  Hao Chen, Ran Tao, Yue Fan, Yidong Wang,
                  <em><b>Jindong Wang</b></em
                  ><sup>#</sup>
                  , Bernt Schiele, Xing Xie, Bhiksha Raj, and Marios Savvides
                </div>
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                  style="
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                >
                  <em
                    >International Conference on Learning Representations
                    (ICLR)</em
                  >
                  2023 | [
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                    >arXiv</a
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                    >Zhihu</a
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                <div
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                >
                  On the Robustness of ChatGPT: An Adversarial and
                  Out-of-distribution Perspective
                </div>
                <div
                  class="author"
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                >
                  <em><b>Jindong Wang</b></em
                  ><sup>#</sup>
                  , Xixu Hu, Wenxin Hou, Hao Chen, Runkai Zheng, Yidong Wang,
                  Linyi Yang, Haojun Huang, Wei Ye, Xiubo Geng, Binxin Jiao, Yue
                  Zhang, and Xing Xie
                </div>
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                >
                  <em
                    >ICLR workshop on Trustworthy and Reliable Large-Scale
                    Machine Learning Models (ICLR 2023 workshop)</em
                  >
                  2023 | [
                  <a
                    href="#"
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                    >arXiv</a
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                    >Code</a
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                    >Zhihu</a
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                >
                  FedCLIP: Fast Generalization and Personalization for CLIP in
                  Federated Learning
                </div>
                <div
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                >
                  Wang Lu, Xixu Hu,
                  <em><b>Jindong Wang</b></em
                  ><sup>#</sup>
                  , and Xing Xie
                </div>
                <div
                  style="
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                  "
                >
                  <em
                    >ICLR workshop on Trustworthy and Reliable Large-Scale
                    Machine Learning Models (ICLR 2023 workshop)</em
                  >
                  2023 | [
                  <a
                    href="#"
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                    >arXiv</a
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              <div id="li2023mutual" class="col-sm-8" style="max-width: 100%">
                <div
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                >
                  A mutual learning framework for pruned and quantized networks
                </div>
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                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Xiaohai Li, Yigiang Chen, and <em><b>Jindong Wang#</b></em>
                </div>
                <div
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: -0.1em;
                  "
                >
                  <em>Journal of Computer Science &amp; Technology</em>
                  2023 | [
                  <!--  -->
                  ]
                </div>
                <!-- <div class="links" style="padding-top: 0em; padding-bottom: 0em; height: 30px; margin-bottom: -.5em;"> -->
                <!--  -->
                <!-- </div> -->
                <!-- Hidden abstract block -->
                <!-- Hidden bibtex block -->
              </div>
              <!-- </div> -->
            </li>
          </ol>
          <div>2022</div>
          <ol class="bibliography">
            <li>
              <!-- <div class="row"> -->
              <!-- <div class="col-sm-1 abbr">
  </div> -->
              <div id="wang2022usb" class="col-sm-8" style="max-width: 100%">
                <div
                  class="title"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  USB: A Unified Semi-supervised Learning Benchmark
                </div>
                <div
                  class="author"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Yidong Wang, Hao Chen, Yue Fan, Wang Sun, Ran Tao, Wenxin Hou,
                  Renjie Wang, Linyi Yang, Zhi Zhou, Lan-Zhe Guo, Heli Qi, Zhen
                  Wu, Yu-Feng Li, Satoshi Nakamura, Wei Ye, Marios Savvides,
                  Bhiksha Raj, Takahiro Shinozaki, Bernt Schiele,
                  <em><b>Jindong Wang</b></em
                  ><sup>#</sup>
                  , Xing Xie, and Yue Zhang
                </div>
                <div
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: -0.1em;
                  "
                >
                  <em
                    >Advances in Neural Information Processing Systems
                    (NeurIPS)</em
                  >
                  2022 | [
                  <a
                    href="#"
                    role="button"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >arXiv</a
                  >
                  <!--  -->
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >Blog</a
                  >
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >Code</a
                  >
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >Zhihu</a
                  >
                  ]
                </div>
                <!-- <div class="links" style="padding-top: 0em; padding-bottom: 0em; height: 30px; margin-bottom: -.5em;"> -->
                <!--  -->
                <!-- </div> -->
                <!-- Hidden abstract block -->
                <!-- Hidden bibtex block -->
              </div>
              <!-- </div> -->
            </li>
            <li>
              <!-- <div class="row"> -->
              <!-- <div class="col-sm-1 abbr">
  </div> -->
              <div id="wang2022margin" class="col-sm-8" style="max-width: 100%">
                <div
                  class="title"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Margin Calibration for Long-Tailed Visual Recognition
                </div>
                <div
                  class="author"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Yidong Wang, Bowen Zhang, Wenxin Hou, Zhen Wu,
                  <em><b>Jindong Wang</b></em
                  ><sup>#</sup>
                  , and Takahiro Shinozaki
                </div>
                <div
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: -0.1em;
                  "
                >
                  <em>Asian Conference on Machine Learning (ACML)</em>
                  2022 | [
                  <a
                    href="#"
                    role="button"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >arXiv</a
                  >
                  <!-- 
        <a class="bibtex btn btn-sm z-depth-0" role="button" style="padding-top: 0em; padding-bottom: 0px;">Bib</a>
         -->
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >Code</a
                  >
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >Zhihu</a
                  >
                  ]
                </div>
                <!-- <div class="links" style="padding-top: 0em; padding-bottom: 0em; height: 30px; margin-bottom: -.5em;"> -->
                <!--  -->
                <!-- </div> -->
                <!-- Hidden abstract block -->
                <!-- Hidden bibtex block -->
                <div class="bibtex hidden">
                  <figure class="highlight">
                    <pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span
                          class="p">{</span><span class="nl">wang2022margin</span><span class="p">,</span>
                        <span class="na">title</span> <span class="p">=</span> <span class="s">{Margin Calibration for
                          Long-Tailed Visual Recognition}</span><span class="p">,</span>
                        <span class="na">author</span> <span class="p">=</span> <span class="s">{Wang, Yidong and Zhang,
                          Bowen and Hou, Wenxin and Wu, Zhen and Wang, Jindong and Shinozaki, Takahiro}</span><span
                          class="p">,</span>
                        <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{Asian Conference on
                          Machine Learning (ACML)}</span><span class="p">,</span>
                        <span class="na">year</span> <span class="p">=</span> <span class="s">{2022}</span><span
                          class="p">,</span>
                        <span class="na">arxiv</span> <span class="p">=</span> <span
                          class="s">{https://arxiv.org/abs/2112.07225}</span><span class="p">,</span>
                        <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">corr</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">zhihu</span> <span class="p">=</span> <span
                          class="s">{https://zhuanlan.zhihu.com/p/579153319}</span><span class="p">,</span>
                        <span class="na">code</span> <span class="p">=</span> <span
                          class="s">{https://github.com/microsoft/robustlearn}</span>
                        <span class="p">}</span></code></pre>
                  </figure>
                </div>
              </div>
              <!-- </div> -->
            </li>
            <li>
              <!-- <div class="row"> -->
              <!-- <div class="col-sm-1 abbr">
  </div> -->
              <div
                id="wang2022exploiting"
                class="col-sm-8"
                style="max-width: 100%"
              >
                <div
                  class="title"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Exploiting Unlabeled Data for Target-Oriented Opinion Words
                  Extraction
                </div>
                <div
                  class="author"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Yidong Wang, Hao Wu, Ao Liu, Wenxin Hou, Zhen Wu,
                  <em><b>Jindong Wang</b></em>
                  , Takahiro Shinozaki, Manabu Okumura, and Yue Zhang
                </div>
                <div
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: -0.1em;
                  "
                >
                  <em
                    >International Conference on Computational Linguistics
                    (COLING)</em
                  >
                  2022 | [
                  <a
                    href="#"
                    role="button"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >arXiv</a
                  >
                  <!--  -->
                  <a href="#" style="padding-top: 0em; padding-bottom: 0px"
                    >PDF</a
                  >
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >Code</a
                  >
                  ]
                </div>
                <!-- <div class="links" style="padding-top: 0em; padding-bottom: 0em; height: 30px; margin-bottom: -.5em;"> -->
                <!--  -->
                <!-- </div> -->
                <!-- Hidden abstract block -->
                <!-- Hidden bibtex block -->
              </div>
              <!-- </div> -->
            </li>
            <li>
              <!-- <div class="row"> -->
              <!-- <div class="col-sm-1 abbr">
    <abbr class="badge">TKDE</abbr>
  </div> -->
              <div
                id="wang2022generalizing"
                class="col-sm-8"
                style="max-width: 100%"
              >
                <div
                  class="title"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Generalizing to Unseen Domains: A Survey on Domain
                  Generalization
                </div>
                <div
                  class="author"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  <em><b>Jindong Wang</b></em>
                  , Cuiling Lan, Chang Liu, Yidong Ouyang, Tao Qin, Wang Lu,
                  Yiqiang Chen, Wenjun Zeng, and Philip S. Yu
                </div>
                <div
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: -0.1em;
                  "
                >
                  <em
                    >IEEE Transactions on Knowledge and Data Engineering
                    (TKDE)</em
                  >
                  2022 | [
                  <a
                    href="#"
                    role="button"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >arXiv</a
                  >
                  <!-- 
        <a class="bibtex btn btn-sm z-depth-0" role="button" style="padding-top: 0em; padding-bottom: 0px;">Bib</a>
         -->
                  <a href="#" style="padding-top: 0em; padding-bottom: 0px"
                    >PDF</a
                  >
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >Code</a
                  >
                  <a href="#" style="padding-top: 0em; padding-bottom: 0px"
                    >Slides</a
                  >
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >Website</a
                  >
                  ]
                </div>
                <!-- <div class="links" style="padding-top: 0em; padding-bottom: 0em; height: 30px; margin-bottom: -.5em;"> -->
                <!--  -->
                <!-- </div> -->
                <!-- Hidden abstract block -->
                <!-- Hidden bibtex block -->
                <div class="bibtex hidden">
                  <figure class="highlight">
                    <pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span
                          class="p">{</span><span class="nl">wang2022generalizing</span><span class="p">,</span>
                        <span class="na">title</span> <span class="p">=</span> <span class="s">{Generalizing to Unseen
                          Domains: A Survey on Domain Generalization}</span><span class="p">,</span>
                        <span class="na">author</span> <span class="p">=</span> <span class="s">{Wang, Jindong and Lan,
                          Cuiling and Liu, Chang and Ouyang, Yidong and Qin, Tao and Lu, Wang and Chen, Yiqiang and
                          Zeng, Wenjun and Yu, Philip S.}</span><span class="p">,</span>
                        <span class="na">journal</span> <span class="p">=</span> <span class="s">{IEEE Transactions on
                          Knowledge and Data Engineering (TKDE)}</span><span class="p">,</span>
                        <span class="na">year</span> <span class="p">=</span> <span class="s">{2022}</span><span
                          class="p">,</span>
                        <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">abbr</span> <span class="p">=</span> <span class="s">{TKDE}</span><span
                          class="p">,</span>
                        <span class="na">arxiv</span> <span class="p">=</span> <span
                          class="s">{https://arxiv.org/abs/2103.03097}</span><span class="p">,</span>
                        <span class="na">code</span> <span class="p">=</span> <span
                          class="s">{https://github.com/jindongwang/transferlearning/tree/master/code/DeepDG}</span><span
                          class="p">,</span>
                        <span class="na">slides</span> <span class="p">=</span> <span
                          class="s">{DGtutorial_ijcai22.pdf}</span><span class="p">,</span>
                        <span class="na">selected</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">pdf</span> <span class="p">=</span> <span
                          class="s">{DG_survey_TKDE22.pdf}</span><span class="p">,</span>
                        <span class="na">website</span> <span class="p">=</span> <span
                          class="s">{https://dgresearch.github.io/}</span>
                        <span class="p">}</span></code></pre>
                  </figure>
                </div>
              </div>
              <!-- </div> -->
            </li>
            <li>
              <!-- <div class="row"> -->
              <!-- <div class="col-sm-1 abbr">
    <abbr class="badge">TMLR</abbr>
  </div> -->
              <div id="lu2022domain" class="col-sm-8" style="max-width: 100%">
                <div
                  class="title"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Domain-invariant Feature Exploration for Domain Generalization
                </div>
                <div
                  class="author"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Wang Lu,
                  <em><b>Jindong Wang</b></em
                  ><sup>#</sup>
                  , Haoliang Li, Yiqiang Chen, and Xing Xie
                </div>
                <div
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: -0.1em;
                  "
                >
                  <em>Transactions on Machine Learning Research (TMLR)</em>
                  2022 | [
                  <a
                    href="#"
                    role="button"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >arXiv</a
                  >
                  <!-- 
        <a class="bibtex btn btn-sm z-depth-0" role="button" style="padding-top: 0em; padding-bottom: 0px;">Bib</a>
         -->
                  <a href="#" style="padding-top: 0em; padding-bottom: 0px"
                    >PDF</a
                  >
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >Website</a
                  >
                  ]
                </div>
                <!-- <div class="links" style="padding-top: 0em; padding-bottom: 0em; height: 30px; margin-bottom: -.5em;"> -->
                <!--  -->
                <!-- </div> -->
                <!-- Hidden abstract block -->
                <!-- Hidden bibtex block -->
                <div class="bibtex hidden">
                  <figure class="highlight">
                    <pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span
                          class="p">{</span><span class="nl">lu2022domain</span><span class="p">,</span>
                        <span class="na">title</span> <span class="p">=</span> <span class="s">{Domain-invariant Feature
                          Exploration for Domain Generalization }</span><span class="p">,</span>
                        <span class="na">author</span> <span class="p">=</span> <span class="s">{Lu, Wang and Wang,
                          Jindong and Li, Haoliang and Chen, Yiqiang and Xie, Xing}</span><span class="p">,</span>
                        <span class="na">journal</span> <span class="p">=</span> <span class="s">{Transactions on
                          Machine Learning Research (TMLR)}</span><span class="p">,</span>
                        <span class="na">year</span> <span class="p">=</span> <span class="s">{2022}</span><span
                          class="p">,</span>
                        <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">corr</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">abbr</span> <span class="p">=</span> <span class="s">{TMLR}</span><span
                          class="p">,</span>
                        <span class="na">website</span> <span class="p">=</span> <span
                          class="s">{https://openreview.net/forum?id=0xENE7HiYm}</span><span class="p">,</span>
                        <span class="na">arxiv</span> <span class="p">=</span> <span
                          class="s">{https://arxiv.org/abs/2207.12020}</span><span class="p">,</span>
                        <span class="na">pdf</span> <span class="p">=</span> <span class="s">{tmlr22-difex.pdf}</span>
                        <span class="p">}</span></code></pre>
                  </figure>
                </div>
              </div>
              <!-- </div> -->
            </li>
            <li>
              <!-- <div class="row"> -->
              <!-- <div class="col-sm-1 abbr">
    <abbr class="badge">TIST</abbr>
  </div> -->
              <div id="qin2022domain" class="col-sm-8" style="max-width: 100%">
                <div
                  class="title"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Domain generalization for activity recognition via adaptive
                  feature fusion
                </div>
                <div
                  class="author"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Xin Qin,
                  <em><b>Jindong Wang</b></em
                  ><sup>#</sup>
                  , Yiqiang Chen, Wang Lu, and Xinlong Jiang
                </div>
                <div
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: -0.1em;
                  "
                >
                  <em
                    >ACM Transactions on Intelligent Systems and Technology
                    (TIST)</em
                  >
                  2022 | [
                  <a
                    href="#"
                    role="button"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >arXiv</a
                  >
                  <!-- 
        <a class="bibtex btn btn-sm z-depth-0" role="button" style="padding-top: 0em; padding-bottom: 0px;">Bib</a>
         -->
                  <a href="#" style="padding-top: 0em; padding-bottom: 0px"
                    >PDF</a
                  >
                  ]
                </div>
                <!-- <div class="links" style="padding-top: 0em; padding-bottom: 0em; height: 30px; margin-bottom: -.5em;"> -->
                <!--  -->
                <!-- </div> -->
                <!-- Hidden abstract block -->
                <!-- Hidden bibtex block -->
                <div class="bibtex hidden">
                  <figure class="highlight">
                    <pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span
                          class="p">{</span><span class="nl">qin2022domain</span><span class="p">,</span>
                        <span class="na">title</span> <span class="p">=</span> <span class="s">{Domain generalization
                          for activity recognition via adaptive feature fusion}</span><span class="p">,</span>
                        <span class="na">author</span> <span class="p">=</span> <span class="s">{Qin, Xin and Wang,
                          Jindong and Chen, Yiqiang and Lu, Wang and Jiang, Xinlong}</span><span class="p">,</span>
                        <span class="na">journal</span> <span class="p">=</span> <span class="s">{ACM Transactions on
                          Intelligent Systems and Technology (TIST)}</span><span class="p">,</span>
                        <span class="na">year</span> <span class="p">=</span> <span class="s">{2022}</span><span
                          class="p">,</span>
                        <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">abbr</span> <span class="p">=</span> <span class="s">{TIST}</span><span
                          class="p">,</span>
                        <span class="na">corr</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">arxiv</span> <span class="p">=</span> <span
                          class="s">{https://arxiv.org/abs/2207.11221}</span><span class="p">,</span>
                        <span class="na">pdf</span> <span class="p">=</span> <span class="s">{tist22-affar.pdf}</span>
                        <span class="p">}</span></code></pre>
                  </figure>
                </div>
              </div>
              <!-- </div> -->
            </li>
            <li>
              <!-- <div class="row"> -->
              <!-- <div class="col-sm-1 abbr">
    <abbr class="badge">TKDE</abbr>
  </div> -->
              <div id="zhu2022memory" class="col-sm-8" style="max-width: 100%">
                <div
                  class="title"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Memory-Guided Multi-View Multi-Domain Fake News Detection
                </div>
                <div
                  class="author"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Yongchun Zhu, Qiang Sheng, Juan Cao, Qiong Nan, Kai Shu,
                  Minghui Wu,
                  <em><b>Jindong Wang</b></em>
                  , and Fuzhen Zhuang
                </div>
                <div
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: -0.1em;
                  "
                >
                  <em
                    >IEEE Transactions on Knowledge and Data Engineering
                    (TKDE)</em
                  >
                  2022 | [
                  <a
                    href="#"
                    role="button"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >arXiv</a
                  >
                  <!-- 
        <a class="bibtex btn btn-sm z-depth-0" role="button" style="padding-top: 0em; padding-bottom: 0px;">Bib</a>
         -->
                  <a href="#" style="padding-top: 0em; padding-bottom: 0px"
                    >PDF</a
                  >
                  ]
                </div>
                <!-- <div class="links" style="padding-top: 0em; padding-bottom: 0em; height: 30px; margin-bottom: -.5em;"> -->
                <!--  -->
                <!-- </div> -->
                <!-- Hidden abstract block -->
                <!-- Hidden bibtex block -->
                <div class="bibtex hidden">
                  <figure class="highlight">
                    <pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span
                          class="p">{</span><span class="nl">zhu2022memory</span><span class="p">,</span>
                        <span class="na">title</span> <span class="p">=</span> <span class="s">{Memory-Guided Multi-View
                          Multi-Domain Fake News Detection}</span><span class="p">,</span>
                        <span class="na">author</span> <span class="p">=</span> <span class="s">{Zhu, Yongchun and
                          Sheng, Qiang and Cao, Juan and Nan, Qiong and Shu, Kai and Wu, Minghui and Wang, Jindong and
                          Zhuang, Fuzhen}</span><span class="p">,</span>
                        <span class="na">journal</span> <span class="p">=</span> <span class="s">{IEEE Transactions on
                          Knowledge and Data Engineering (TKDE)}</span><span class="p">,</span>
                        <span class="na">year</span> <span class="p">=</span> <span class="s">{2022}</span><span
                          class="p">,</span>
                        <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">abbr</span> <span class="p">=</span> <span class="s">{TKDE}</span><span
                          class="p">,</span>
                        <span class="na">arxiv</span> <span class="p">=</span> <span
                          class="s">{https://arxiv.org/abs/2206.12808}</span><span class="p">,</span>
                        <span class="na">pdf</span> <span class="p">=</span> <span class="s">{tkde22-mdfend.pdf}</span>
                        <span class="p">}</span></code></pre>
                  </figure>
                </div>
              </div>
              <!-- </div> -->
            </li>
            <li>
              <!-- <div class="row"> -->
              <!-- <div class="col-sm-1 abbr">
    <abbr class="badge">IS</abbr>
  </div> -->
              <div
                id="zhu2022decoupled"
                class="col-sm-8"
                style="max-width: 100%"
              >
                <div
                  class="title"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Decoupled Federated Learning for ASR with Non-IID Data
                </div>
                <div
                  class="author"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Han Zhu,
                  <em><b>Jindong Wang</b></em>
                  , Gaofeng Cheng, Pengyuan Zhang, and Yonghong Yan
                </div>
                <div
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: -0.1em;
                  "
                >
                  <em>Interspeech</em>
                  2022 | [
                  <a
                    href="#"
                    role="button"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >arXiv</a
                  >
                  <!--  -->
                  <a href="#" style="padding-top: 0em; padding-bottom: 0px"
                    >PDF</a
                  >
                  ]
                </div>
                <!-- <div class="links" style="padding-top: 0em; padding-bottom: 0em; height: 30px; margin-bottom: -.5em;"> -->
                <!--  -->
                <!-- </div> -->
                <!-- Hidden abstract block -->
                <!-- Hidden bibtex block -->
              </div>
              <!-- </div> -->
            </li>
            <li>
              <!-- <div class="row"> -->
              <!-- <div class="col-sm-1 abbr">
    <abbr class="badge">IS</abbr>
  </div> -->
              <div id="zhu2022wav2vec" class="col-sm-8" style="max-width: 100%">
                <div
                  class="title"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Wav2vec-s: Semi-supervised pre-training for speech recognition
                </div>
                <div
                  class="author"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Han Zhu, Li Wang, Ying Hou,
                  <em><b>Jindong Wang</b></em>
                  , Gaofeng Cheng, Pengyuan Zhang, and Yonghong Yan
                </div>
                <div
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: -0.1em;
                  "
                >
                  <em>Interspeech</em>
                  2022 | [
                  <a
                    href="#"
                    role="button"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >arXiv</a
                  >
                  <!--  -->
                  <a href="#" style="padding-top: 0em; padding-bottom: 0px"
                    >PDF</a
                  >
                  ]
                </div>
                <!-- <div class="links" style="padding-top: 0em; padding-bottom: 0em; height: 30px; margin-bottom: -.5em;"> -->
                <!--  -->
                <!-- </div> -->
                <!-- Hidden abstract block -->
                <!-- Hidden bibtex block -->
              </div>
              <!-- </div> -->
            </li>
            <li>
              <!-- <div class="row"> -->
              <!-- <div class="col-sm-1 abbr">
    <abbr class="badge">TBD</abbr>
  </div> -->
              <div
                id="lu2022personalized"
                class="col-sm-8"
                style="max-width: 100%"
              >
                <div
                  class="title"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Personalized Federated Learning with Adaptive Batchnorm for
                  Healthcare
                </div>
                <div
                  class="author"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Wang Lu,
                  <em><b>Jindong Wang</b></em
                  ><sup>#</sup>
                  , Yiqiang Chen, Xin Qin, Renjun Xu, Dimitrios Dimitriadis, and
                  Tao Qin
                </div>
                <div
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: -0.1em;
                  "
                >
                  <em>IEEE Transactions on Big Data (TBD)</em>
                  2022 | [
                  <a
                    href="#"
                    role="button"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >arXiv</a
                  >
                  <!-- 
        <a class="bibtex btn btn-sm z-depth-0" role="button" style="padding-top: 0em; padding-bottom: 0px;">Bib</a>
         -->
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >HTML</a
                  >
                  <a href="#" style="padding-top: 0em; padding-bottom: 0px"
                    >PDF</a
                  >
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >Code</a
                  >
                  ]
                </div>
                <!-- <div class="links" style="padding-top: 0em; padding-bottom: 0em; height: 30px; margin-bottom: -.5em;"> -->
                <!--  -->
                <!-- </div> -->
                <!-- Hidden abstract block -->
                <!-- Hidden bibtex block -->
                <div class="bibtex hidden">
                  <figure class="highlight">
                    <pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span
                          class="p">{</span><span class="nl">lu2022personalized</span><span class="p">,</span>
                        <span class="na">title</span> <span class="p">=</span> <span class="s">{Personalized Federated
                          Learning with Adaptive Batchnorm for Healthcare}</span><span class="p">,</span>
                        <span class="na">author</span> <span class="p">=</span> <span class="s">{Lu, Wang and Wang,
                          Jindong and Chen, Yiqiang and Qin, Xin and Xu, Renjun and Dimitriadis, Dimitrios and Qin,
                          Tao}</span><span class="p">,</span>
                        <span class="na">journal</span> <span class="p">=</span> <span class="s">{IEEE Transactions on
                          Big Data (TBD)}</span><span class="p">,</span>
                        <span class="na">year</span> <span class="p">=</span> <span class="s">{2022}</span><span
                          class="p">,</span>
                        <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">corr</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">abbr</span> <span class="p">=</span> <span class="s">{TBD}</span><span
                          class="p">,</span>
                        <span class="na">arxiv</span> <span class="p">=</span> <span
                          class="s">{https://arxiv.org/abs/2112.00734}</span><span class="p">,</span>
                        <span class="na">pdf</span> <span class="p">=</span> <span
                          class="s">{tbd22-fedap.pdf}</span><span class="p">,</span>
                        <span class="na">html</span> <span class="p">=</span> <span
                          class="s">{https://ieeexplore.ieee.org/document/9780172}</span><span class="p">,</span>
                        <span class="na">code</span> <span class="p">=</span> <span
                          class="s">{https://github.com/microsoft/PersonalizedFL}</span>
                        <span class="p">}</span></code></pre>
                  </figure>
                </div>
              </div>
              <!-- </div> -->
            </li>
            <li>
              <!-- <div class="row"> -->
              <!-- <div class="col-sm-1 abbr">
    <abbr class="badge">IMWUT</abbr>
  </div> -->
              <div id="lu2022semantic" class="col-sm-8" style="max-width: 100%">
                <div
                  class="title"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Semantic-Discriminative Mixup for Generalizable Sensor-based
                  Cross-domain Activity Recognition
                </div>
                <div
                  class="author"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Wang Lu,
                  <em><b>Jindong Wang</b></em
                  ><sup>#</sup>
                  , Yiqiang Chen, Sinno Pan, Chunyu Hu, and Xin Qin
                </div>
                <div
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: -0.1em;
                  "
                >
                  <em
                    >Proceedings of the ACM on Interactive, Mobile, Wearable,
                    and Ubiquitous Technologies (IMWUT, i.e., UbiComp)</em
                  >
                  2022 | [
                  <a
                    href="#"
                    role="button"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >arXiv</a
                  >
                  <!-- 
        <a class="bibtex btn btn-sm z-depth-0" role="button" style="padding-top: 0em; padding-bottom: 0px;">Bib</a>
         -->
                  <a href="#" style="padding-top: 0em; padding-bottom: 0px"
                    >PDF</a
                  >
                  ]
                </div>
                <!-- <div class="links" style="padding-top: 0em; padding-bottom: 0em; height: 30px; margin-bottom: -.5em;"> -->
                <!--  -->
                <!-- </div> -->
                <!-- Hidden abstract block -->
                <!-- Hidden bibtex block -->
                <div class="bibtex hidden">
                  <figure class="highlight">
                    <pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span
                          class="p">{</span><span class="nl">lu2022semantic</span><span class="p">,</span>
                        <span class="na">title</span> <span class="p">=</span> <span class="s">{Semantic-Discriminative
                          Mixup for Generalizable Sensor-based Cross-domain Activity Recognition}</span><span
                          class="p">,</span>
                        <span class="na">author</span> <span class="p">=</span> <span class="s">{Lu, Wang and Wang,
                          Jindong and Chen, Yiqiang and Pan, Sinno and Hu, Chunyu and Qin, Xin}</span><span
                          class="p">,</span>
                        <span class="na">journal</span> <span class="p">=</span> <span class="s">{Proceedings of the ACM
                          on Interactive, Mobile, Wearable, and Ubiquitous Technologies (IMWUT, i.e.,
                          UbiComp)}</span><span class="p">,</span>
                        <span class="na">year</span> <span class="p">=</span> <span class="s">{2022}</span><span
                          class="p">,</span>
                        <span class="na">abbr</span> <span class="p">=</span> <span class="s">{IMWUT}</span><span
                          class="p">,</span>
                        <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">corr</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">selected</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">arxiv</span> <span class="p">=</span> <span
                          class="s">{http://arxiv.org/abs/2206.06629}</span><span class="p">,</span>
                        <span class="na">pdf</span> <span class="p">=</span> <span class="s">{imwut22-sdmix.pdf}</span>
                        <span class="p">}</span></code></pre>
                  </figure>
                </div>
              </div>
              <!-- </div> -->
            </li>
            <li>
              <!-- <div class="row"> -->
              <!-- <div class="col-sm-1 abbr">
    <abbr class="badge">TKDE</abbr>
  </div> -->
              <div
                id="zhang2022adaptive"
                class="col-sm-8"
                style="max-width: 100%"
              >
                <div
                  class="title"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Adaptive Memory Networks with Self-supervised Learning for
                  Unsupervised Anomaly Detection
                </div>
                <div
                  class="author"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Yuxin Zhang,
                  <em><b>Jindong Wang</b></em
                  ><sup>#</sup>
                  , Yiqiang Chen, Han Yu, and Tao Qin
                </div>
                <div
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: -0.1em;
                  "
                >
                  <em
                    >IEEE Transactions on Knowledge and Data Engineering
                    (TKDE)</em
                  >
                  2022 | [
                  <a
                    href="#"
                    role="button"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >arXiv</a
                  >
                  <!-- 
        <a class="bibtex btn btn-sm z-depth-0" role="button" style="padding-top: 0em; padding-bottom: 0px;">Bib</a>
         -->
                  <a href="#" style="padding-top: 0em; padding-bottom: 0px"
                    >PDF</a
                  >
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >Code</a
                  >
                  ]
                </div>
                <!-- <div class="links" style="padding-top: 0em; padding-bottom: 0em; height: 30px; margin-bottom: -.5em;"> -->
                <!--  -->
                <!-- </div> -->
                <!-- Hidden abstract block -->
                <!-- Hidden bibtex block -->
                <div class="bibtex hidden">
                  <figure class="highlight">
                    <pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span
                          class="p">{</span><span class="nl">zhang2022adaptive</span><span class="p">,</span>
                        <span class="na">title</span> <span class="p">=</span> <span class="s">{Adaptive Memory Networks
                          with Self-supervised Learning for Unsupervised Anomaly Detection}</span><span
                          class="p">,</span>
                        <span class="na">author</span> <span class="p">=</span> <span class="s">{Zhang, Yuxin and Wang,
                          Jindong and Chen, Yiqiang and Yu, Han and Qin, Tao}</span><span class="p">,</span>
                        <span class="na">journal</span> <span class="p">=</span> <span class="s">{IEEE Transactions on
                          Knowledge and Data Engineering (TKDE)}</span><span class="p">,</span>
                        <span class="na">year</span> <span class="p">=</span> <span class="s">{2022}</span><span
                          class="p">,</span>
                        <span class="na">abbr</span> <span class="p">=</span> <span class="s">{TKDE}</span><span
                          class="p">,</span>
                        <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">corr</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">selected</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">arxiv</span> <span class="p">=</span> <span
                          class="s">{https://arxiv.org/abs/2201.00464}</span><span class="p">,</span>
                        <span class="na">pdf</span> <span class="p">=</span> <span
                          class="s">{tkde22_amsl.pdf}</span><span class="p">,</span>
                        <span class="na">code</span> <span class="p">=</span> <span
                          class="s">{https://github.com/zhangyuxin621/AMSL}</span>
                        <span class="p">}</span></code></pre>
                  </figure>
                </div>
              </div>
              <!-- </div> -->
            </li>
            <li>
              <!-- <div class="row"> -->
              <!-- <div class="col-sm-1 abbr">
    <abbr class="badge">TASLP</abbr>
  </div> -->
              <div
                id="hou2022exploiting"
                class="col-sm-8"
                style="max-width: 100%"
              >
                <div
                  class="title"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Exploiting Adapters for Cross-lingual Low-resource Speech
                  Recognition
                </div>
                <div
                  class="author"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Wenxin Hou, Han Zhu, Yidong Wang,
                  <em><b>Jindong Wang</b></em
                  ><sup>#</sup>
                  , Tao Qin, Renjun Xu, and Takahiro Shinozaki
                </div>
                <div
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: -0.1em;
                  "
                >
                  <em
                    >IEEE/ACM Transactions on Audio, Speech and Language
                    Processing (TASLP)</em
                  >
                  2022 | [
                  <a
                    href="#"
                    role="button"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >arXiv</a
                  >
                  <!-- 
        <a class="bibtex btn btn-sm z-depth-0" role="button" style="padding-top: 0em; padding-bottom: 0px;">Bib</a>
         -->
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >Code</a
                  >
                  ]
                </div>
                <!-- <div class="links" style="padding-top: 0em; padding-bottom: 0em; height: 30px; margin-bottom: -.5em;"> -->
                <!--  -->
                <!-- </div> -->
                <!-- Hidden abstract block -->
                <!-- Hidden bibtex block -->
                <div class="bibtex hidden">
                  <figure class="highlight">
                    <pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span
                          class="p">{</span><span class="nl">hou2022exploiting</span><span class="p">,</span>
                        <span class="na">title</span> <span class="p">=</span> <span class="s">{Exploiting Adapters for
                          Cross-lingual Low-resource Speech Recognition}</span><span class="p">,</span>
                        <span class="na">author</span> <span class="p">=</span> <span class="s">{Hou, Wenxin and Zhu,
                          Han and Wang, Yidong and Wang, Jindong and Qin, Tao and Xu, Renjun and Shinozaki,
                          Takahiro}</span><span class="p">,</span>
                        <span class="na">journal</span> <span class="p">=</span> <span class="s">{IEEE/ACM Transactions
                          on Audio, Speech and Language Processing (TASLP)}</span><span class="p">,</span>
                        <span class="na">year</span> <span class="p">=</span> <span class="s">{2022}</span><span
                          class="p">,</span>
                        <span class="na">abbr</span> <span class="p">=</span> <span class="s">{TASLP}</span><span
                          class="p">,</span>
                        <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">arxiv</span> <span class="p">=</span> <span
                          class="s">{https://arxiv.org/abs/2105.11905}</span><span class="p">,</span>
                        <span class="na">corr</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">code</span> <span class="p">=</span> <span
                          class="s">{https://github.com/jindongwang/transferlearning/tree/master/code/ASR/Adapter}</span>
                        <span class="p">}</span></code></pre>
                  </figure>
                </div>
              </div>
              <!-- </div> -->
            </li>
            <li>
              <!-- <div class="row"> -->
              <!-- <div class="col-sm-1 abbr">
  </div> -->
              <div
                id="xu2022hierarchical"
                class="col-sm-8"
                style="max-width: 100%"
              >
                <div
                  class="title"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Hierarchical knowledge amalgamation with dual discriminative
                  feature alignment
                </div>
                <div
                  class="author"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Renjun Xu, Shuoying Liang, Lanyu Wen, Zhitong Guo, Xinyue
                  Huang, Mingli Song,
                  <em><b>Jindong Wang</b></em>
                  , Xiaoxiao Xu, and Huajun Chen
                </div>
                <div
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: -0.1em;
                  "
                >
                  <em>Information Sciences</em>
                  2022 | [
                  <!--  -->
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >Website</a
                  >
                  ]
                </div>
                <!-- <div class="links" style="padding-top: 0em; padding-bottom: 0em; height: 30px; margin-bottom: -.5em;"> -->
                <!--  -->
                <!-- </div> -->
                <!-- Hidden abstract block -->
                <!-- Hidden bibtex block -->
              </div>
              <!-- </div> -->
            </li>
            <li>
              <!-- <div class="row"> -->
              <!-- <div class="col-sm-1 abbr">
  </div> -->
              <div
                id="chen2022metafed"
                class="col-sm-8"
                style="max-width: 100%"
              >
                <div
                  class="title"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  MetaFed: Federated Learning among Federations with Cyclic
                  Knowledge Distillation for Personalized Healthcare
                </div>
                <div
                  class="author"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Yiqiang Chen, Wang Lu, Xin Qin,
                  <em><b>Jindong Wang</b></em>
                  , and Xing Xie
                </div>
                <div
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: -0.1em;
                  "
                >
                  <em>IJCAI’22 federated learning workshop (FL-IJCAI)</em>
                  2022 | [
                  <a
                    href="#"
                    role="button"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >arXiv</a
                  >
                  <!--  -->
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >Code</a
                  >
                  ]
                </div>
                <div><b>(Innovation Award)</b></div>
                <!-- <div class="links" style="padding-top: 0em; padding-bottom: 0em; height: 30px; margin-bottom: -.5em;"> -->
                <!--  -->
                <!-- </div> -->
                <!-- Hidden abstract block -->
                <!-- Hidden bibtex block -->
              </div>
              <!-- </div> -->
            </li>
            <li>
              <!-- <div class="row"> -->
              <!-- <div class="col-sm-1 abbr">
    <abbr class="badge">ICASSP</abbr>
  </div> -->
              <div id="lu2022local" class="col-sm-8" style="max-width: 100%">
                <div
                  class="title"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Local and global alignments for generalizable sensor-based
                  human activity recognition
                </div>
                <div
                  class="author"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Wang Lu,
                  <em><b>Jindong Wang</b></em
                  ><sup>#</sup>
                  , and Yiqiang Chen
                </div>
                <div
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: -0.1em;
                  "
                >
                  <em
                    >IEEE International Conference on Acoustics, Speech and
                    Signal Processing (ICASSP)</em
                  >
                  2022 | [
                  <!-- 
        <a class="bibtex btn btn-sm z-depth-0" role="button" style="padding-top: 0em; padding-bottom: 0px;">Bib</a>
         -->
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >HTML</a
                  >
                  ]
                </div>
                <!-- <div class="links" style="padding-top: 0em; padding-bottom: 0em; height: 30px; margin-bottom: -.5em;"> -->
                <!--  -->
                <!-- </div> -->
                <!-- Hidden abstract block -->
                <!-- Hidden bibtex block -->
                <div class="bibtex hidden">
                  <figure class="highlight">
                    <pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span
                          class="p">{</span><span class="nl">lu2022local</span><span class="p">,</span>
                        <span class="na">title</span> <span class="p">=</span> <span class="s">{Local and global
                          alignments for generalizable sensor-based human activity recognition}</span><span
                          class="p">,</span>
                        <span class="na">author</span> <span class="p">=</span> <span class="s">{Lu, Wang and Wang,
                          Jindong and Chen, Yiqiang}</span><span class="p">,</span>
                        <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{IEEE International
                          Conference on Acoustics, Speech and Signal Processing (ICASSP)}</span><span class="p">,</span>
                        <span class="na">year</span> <span class="p">=</span> <span class="s">{2022}</span><span
                          class="p">,</span>
                        <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">corr</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">abbr</span> <span class="p">=</span> <span class="s">{ICASSP}</span><span
                          class="p">,</span>
                        <span class="na">html</span> <span class="p">=</span> <span
                          class="s">{https://ieeexplore.ieee.org/abstract/document/9747363}</span>
                        <span class="p">}</span></code></pre>
                  </figure>
                </div>
              </div>
              <!-- </div> -->
            </li>
            <li>
              <!-- <div class="row"> -->
              <!-- <div class="col-sm-1 abbr">
    <abbr class="badge">ICSE</abbr>
  </div> -->
              <div id="zhang2022remos" class="col-sm-8" style="max-width: 100%">
                <div
                  class="title"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  ReMoS: Reducing Defect Inheritance in Transfer Learning via
                  Relevant Model Slicing
                </div>
                <div
                  class="author"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Ziqi Zhang, Yuanchun Li,
                  <em><b>Jindong Wang</b></em>
                  , Bingyan Liu, Ding Li, Xiangqun Chen, Yao Guo, and Yunxin Liu
                </div>
                <div
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: -0.1em;
                  "
                >
                  <em
                    >44th International Conference on Software Engineering
                    (ICSE)</em
                  >
                  2022 | [
                  <!-- 
        <a class="bibtex btn btn-sm z-depth-0" role="button" style="padding-top: 0em; padding-bottom: 0px;">Bib</a>
         -->
                  <a href="#" style="padding-top: 0em; padding-bottom: 0px"
                    >PDF</a
                  >
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >Code</a
                  >
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >Video</a
                  >
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >Zhihu</a
                  >
                  ]
                </div>
                <!-- <div class="links" style="padding-top: 0em; padding-bottom: 0em; height: 30px; margin-bottom: -.5em;"> -->
                <!--  -->
                <!-- </div> -->
                <!-- Hidden abstract block -->
                <!-- Hidden bibtex block -->
                <div class="bibtex hidden">
                  <figure class="highlight">
                    <pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span
                          class="p">{</span><span class="nl">zhang2022remos</span><span class="p">,</span>
                        <span class="na">title</span> <span class="p">=</span> <span class="s">{ReMoS: Reducing Defect
                          Inheritance in Transfer Learning via Relevant Model Slicing}</span><span class="p">,</span>
                        <span class="na">author</span> <span class="p">=</span> <span class="s">{Zhang, Ziqi and Li,
                          Yuanchun and Wang, Jindong and Liu, Bingyan and Li, Ding and Chen, Xiangqun and Guo, Yao and
                          Liu, Yunxin}</span><span class="p">,</span>
                        <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{44th International
                          Conference on Software Engineering (ICSE)}</span><span class="p">,</span>
                        <span class="na">year</span> <span class="p">=</span> <span class="s">{2022}</span><span
                          class="p">,</span>
                        <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">abbr</span> <span class="p">=</span> <span class="s">{ICSE}</span><span
                          class="p">,</span>
                        <span class="na">pdf</span> <span class="p">=</span> <span
                          class="s">{icse22-remos.pdf}</span><span class="p">,</span>
                        <span class="na">code</span> <span class="p">=</span> <span
                          class="s">{https://github.com/jindongwang/transferlearning/tree/master/code/deep/ReMoS}</span><span
                          class="p">,</span>
                        <span class="na">zhihu</span> <span class="p">=</span> <span
                          class="s">{https://zhuanlan.zhihu.com/p/446453487}</span><span class="p">,</span>
                        <span class="na">video</span> <span class="p">=</span> <span
                          class="s">{https://www.bilibili.com/video/BV1mi4y1C7bP}</span><span class="p">,</span>
                        <span class="na">selected</span> <span class="p">=</span> <span class="s">{true}</span>
                        <span class="p">}</span></code></pre>
                  </figure>
                </div>
              </div>
              <!-- </div> -->
            </li>
          </ol>
          <div>2021</div>
          <ol class="bibliography">
            <li>
              <!-- <div class="row"> -->
              <!-- <div class="col-sm-1 abbr">
    <abbr class="badge">TKDE</abbr>
  </div> -->
              <div
                id="zhang2021unsupervised"
                class="col-sm-8"
                style="max-width: 100%"
              >
                <div
                  class="title"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Unsupervised deep anomaly detection for multi-sensor
                  time-series signals
                </div>
                <div
                  class="author"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Yuxin Zhang, Yiqiang Chen,
                  <em><b>Jindong Wang</b></em
                  ><sup>#</sup>
                  , and Zhiwen Pan
                </div>
                <div
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: -0.1em;
                  "
                >
                  <em
                    >IEEE Transactions on Knowledge and Data Engineering
                    (TKDE)</em
                  >
                  2021 | [
                  <a
                    href="#"
                    role="button"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >arXiv</a
                  >
                  <!-- 
        <a class="bibtex btn btn-sm z-depth-0" role="button" style="padding-top: 0em; padding-bottom: 0px;">Bib</a>
         -->
                  <a href="#" style="padding-top: 0em; padding-bottom: 0px"
                    >PDF</a
                  >
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >Zhihu</a
                  >
                  ]
                </div>
                <!-- <div class="links" style="padding-top: 0em; padding-bottom: 0em; height: 30px; margin-bottom: -.5em;"> -->
                <!--  -->
                <!-- </div> -->
                <!-- Hidden abstract block -->
                <!-- Hidden bibtex block -->
                <div class="bibtex hidden">
                  <figure class="highlight">
                    <pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span
                          class="p">{</span><span class="nl">zhang2021unsupervised</span><span class="p">,</span>
                        <span class="na">title</span> <span class="p">=</span> <span class="s">{Unsupervised deep
                          anomaly detection for multi-sensor time-series signals}</span><span class="p">,</span>
                        <span class="na">author</span> <span class="p">=</span> <span class="s">{Zhang, Yuxin and Chen,
                          Yiqiang and Wang, Jindong and Pan, Zhiwen}</span><span class="p">,</span>
                        <span class="na">journal</span> <span class="p">=</span> <span class="s">{IEEE Transactions on
                          Knowledge and Data Engineering (TKDE)}</span><span class="p">,</span>
                        <span class="na">year</span> <span class="p">=</span> <span class="s">{2021}</span><span
                          class="p">,</span>
                        <span class="na">publisher</span> <span class="p">=</span> <span class="s">{IEEE}</span><span
                          class="p">,</span>
                        <span class="na">abbr</span> <span class="p">=</span> <span class="s">{TKDE}</span><span
                          class="p">,</span>
                        <span class="na">arxiv</span> <span class="p">=</span> <span
                          class="s">{https://arxiv.org/abs/2107.12626}</span><span class="p">,</span>
                        <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">corr</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">zhihu</span> <span class="p">=</span> <span
                          class="s">{https://zhuanlan.zhihu.com/p/399411106}</span><span class="p">,</span>
                        <span class="na">pdf</span> <span class="p">=</span> <span class="s">{tkde21.pdf}</span>
                        <span class="p">}</span></code></pre>
                  </figure>
                </div>
              </div>
              <!-- </div> -->
            </li>
            <li>
              <!-- <div class="row"> -->
              <!-- <div class="col-sm-1 abbr">
    <abbr class="badge">NeuCom</abbr>
  </div> -->
              <div id="lu2021cross" class="col-sm-8" style="max-width: 100%">
                <div
                  class="title"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Cross-domain activity recognition via substructural optimal
                  transport
                </div>
                <div
                  class="author"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Wang Lu, Yiqiang Chen,
                  <em><b>Jindong Wang</b></em>
                  , and Xin Qin
                </div>
                <div
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: -0.1em;
                  "
                >
                  <em>Neurocomputing</em>
                  2021 | [
                  <a
                    href="#"
                    role="button"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >arXiv</a
                  >
                  <!-- 
        <a class="bibtex btn btn-sm z-depth-0" role="button" style="padding-top: 0em; padding-bottom: 0px;">Bib</a>
         -->
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >HTML</a
                  >
                  <a href="#" style="padding-top: 0em; padding-bottom: 0px"
                    >PDF</a
                  >
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >Code</a
                  >
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >Zhihu</a
                  >
                  ]
                </div>
                <!-- <div class="links" style="padding-top: 0em; padding-bottom: 0em; height: 30px; margin-bottom: -.5em;"> -->
                <!--  -->
                <!-- </div> -->
                <!-- Hidden abstract block -->
                <!-- Hidden bibtex block -->
                <div class="bibtex hidden">
                  <figure class="highlight">
                    <pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span
                          class="p">{</span><span class="nl">lu2021cross</span><span class="p">,</span>
                        <span class="na">title</span> <span class="p">=</span> <span class="s">{Cross-domain activity
                          recognition via substructural optimal transport}</span><span class="p">,</span>
                        <span class="na">author</span> <span class="p">=</span> <span class="s">{Lu, Wang and Chen,
                          Yiqiang and Wang, Jindong and Qin, Xin}</span><span class="p">,</span>
                        <span class="na">journal</span> <span class="p">=</span> <span
                          class="s">{Neurocomputing}</span><span class="p">,</span>
                        <span class="na">volume</span> <span class="p">=</span> <span class="s">{454}</span><span
                          class="p">,</span>
                        <span class="na">pages</span> <span class="p">=</span> <span class="s">{65--75}</span><span
                          class="p">,</span>
                        <span class="na">year</span> <span class="p">=</span> <span class="s">{2021}</span><span
                          class="p">,</span>
                        <span class="na">publisher</span> <span class="p">=</span> <span
                          class="s">{Elsevier}</span><span class="p">,</span>
                        <span class="na">abbr</span> <span class="p">=</span> <span class="s">{NeuCom}</span><span
                          class="p">,</span>
                        <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">arxiv</span> <span class="p">=</span> <span
                          class="s">{https://arxiv.org/abs/2102.03353}</span><span class="p">,</span>
                        <span class="na">html</span> <span class="p">=</span> <span
                          class="s">{https://www.sciencedirect.com/science/article/abs/pii/S0925231221007025}</span><span
                          class="p">,</span>
                        <span class="na">zhihu</span> <span class="p">=</span> <span
                          class="s">{https://zhuanlan.zhihu.com/p/356904023}</span><span class="p">,</span>
                        <span class="na">code</span> <span class="p">=</span> <span
                          class="s">{https://github.com/jindongwang/transferlearning/tree/master/code/traditional/sot}</span><span
                          class="p">,</span>
                        <span class="na">pdf</span> <span class="p">=</span> <span
                          class="s">{neurocomputing21-sot.pdf}</span>
                        <span class="p">}</span></code></pre>
                  </figure>
                </div>
              </div>
              <!-- </div> -->
            </li>
            <li>
              <!-- <div class="row"> -->
              <!-- <div class="col-sm-1 abbr">
    <abbr class="badge">NeurIPS</abbr>
  </div> -->
              <div
                id="zhang2021flexmatch"
                class="col-sm-8"
                style="max-width: 100%"
              >
                <div
                  class="title"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Flexmatch: Boosting semi-supervised learning with curriculum
                  pseudo labeling
                </div>
                <div
                  class="author"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Bowen Zhang, Yidong Wang, Wenxin Hou, Hao Wu,
                  <em><b>Jindong Wang</b></em
                  ><sup>#</sup>
                  , Manabu Okumura, and Takahiro Shinozaki
                </div>
                <div
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: -0.1em;
                  "
                >
                  <em
                    >Advances in Neural Information Processing Systems
                    (NeurIPS)</em
                  >
                  2021 | [
                  <a
                    href="#"
                    role="button"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >arXiv</a
                  >
                  <!-- 
        <a class="bibtex btn btn-sm z-depth-0" role="button" style="padding-top: 0em; padding-bottom: 0px;">Bib</a>
         -->
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >PDF</a
                  >
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >Code</a
                  >
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >Slides</a
                  >
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >Video</a
                  >
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >Zhihu</a
                  >
                  ]
                </div>
                <!-- <div class="links" style="padding-top: 0em; padding-bottom: 0em; height: 30px; margin-bottom: -.5em;"> -->
                <!--  -->
                <!-- </div> -->
                <!-- Hidden abstract block -->
                <!-- Hidden bibtex block -->
                <div class="bibtex hidden">
                  <figure class="highlight">
                    <pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span
                          class="p">{</span><span class="nl">zhang2021flexmatch</span><span class="p">,</span>
                        <span class="na">title</span> <span class="p">=</span> <span class="s">{Flexmatch: Boosting
                          semi-supervised learning with curriculum pseudo labeling}</span><span class="p">,</span>
                        <span class="na">author</span> <span class="p">=</span> <span class="s">{Zhang, Bowen and Wang,
                          Yidong and Hou, Wenxin and Wu, Hao and Wang, Jindong and Okumura, Manabu and Shinozaki,
                          Takahiro}</span><span class="p">,</span>
                        <span class="na">journal</span> <span class="p">=</span> <span class="s">{Advances in Neural
                          Information Processing Systems (NeurIPS)}</span><span class="p">,</span>
                        <span class="na">volume</span> <span class="p">=</span> <span class="s">{34}</span><span
                          class="p">,</span>
                        <span class="na">year</span> <span class="p">=</span> <span class="s">{2021}</span><span
                          class="p">,</span>
                        <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">corr</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">abbr</span> <span class="p">=</span> <span class="s">{NeurIPS}</span><span
                          class="p">,</span>
                        <span class="na">arxiv</span> <span class="p">=</span> <span
                          class="s">{https://arxiv.org/abs/2110.08263}</span><span class="p">,</span>
                        <span class="na">pdf</span> <span class="p">=</span> <span
                          class="s">{http://jd92.wang/assets/files/flexmatch_nips21.pdf}</span><span class="p">,</span>
                        <span class="na">code</span> <span class="p">=</span> <span
                          class="s">{https://github.com/TorchSSL/TorchSSL}</span><span class="p">,</span>
                        <span class="na">zhihu</span> <span class="p">=</span> <span
                          class="s">{https://zhuanlan.zhihu.com/p/422930830}</span><span class="p">,</span>
                        <span class="na">video</span> <span class="p">=</span> <span
                          class="s">{https://www.youtube.com/watch?v=aYuUwyZl_WY}</span><span class="p">,</span>
                        <span class="na">slides</span> <span class="p">=</span> <span
                          class="s">{https://www.jianguoyun.com/p/DXeFVg8QjKnsBRibj54E}</span><span class="p">,</span>
                        <span class="na">selected</span> <span class="p">=</span> <span class="s">{true}</span>
                        <span class="p">}</span></code></pre>
                  </figure>
                </div>
              </div>
              <!-- </div> -->
            </li>
            <li>
              <!-- <div class="row"> -->
              <!-- <div class="col-sm-1 abbr">
    <abbr class="badge">NeurIPS</abbr>
  </div> -->
              <div
                id="liu2021learning"
                class="col-sm-8"
                style="max-width: 100%"
              >
                <div
                  class="title"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Learning causal semantic representation for
                  out-of-distribution prediction
                </div>
                <div
                  class="author"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Chang Liu, Xinwei Sun,
                  <em><b>Jindong Wang</b></em>
                  , Haoyue Tang, Tao Li, Tao Qin, Wei Chen, and Tie-Yan Liu
                </div>
                <div
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: -0.1em;
                  "
                >
                  <em
                    >Thirty-Fifth Conference on Neural Information Processing
                    Systems (NeurIPS)</em
                  >
                  2021 | [
                  <a
                    href="#"
                    role="button"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >arXiv</a
                  >
                  <!-- 
        <a class="bibtex btn btn-sm z-depth-0" role="button" style="padding-top: 0em; padding-bottom: 0px;">Bib</a>
         -->
                  <a href="#" style="padding-top: 0em; padding-bottom: 0px"
                    >PDF</a
                  >
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >Code</a
                  >
                  ]
                </div>
                <!-- <div class="links" style="padding-top: 0em; padding-bottom: 0em; height: 30px; margin-bottom: -.5em;"> -->
                <!--  -->
                <!-- </div> -->
                <!-- Hidden abstract block -->
                <!-- Hidden bibtex block -->
                <div class="bibtex hidden">
                  <figure class="highlight">
                    <pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span
                          class="p">{</span><span class="nl">liu2021learning</span><span class="p">,</span>
                        <span class="na">title</span> <span class="p">=</span> <span class="s">{Learning causal semantic
                          representation for out-of-distribution prediction}</span><span class="p">,</span>
                        <span class="na">author</span> <span class="p">=</span> <span class="s">{Liu, Chang and Sun,
                          Xinwei and Wang, Jindong and Tang, Haoyue and Li, Tao and Qin, Tao and Chen, Wei and Liu,
                          Tie-Yan}</span><span class="p">,</span>
                        <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{Thirty-Fifth
                          Conference on Neural Information Processing Systems (NeurIPS)}</span><span class="p">,</span>
                        <span class="na">year</span> <span class="p">=</span> <span class="s">{2021}</span><span
                          class="p">,</span>
                        <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">abbr</span> <span class="p">=</span> <span class="s">{NeurIPS}</span><span
                          class="p">,</span>
                        <span class="na">arxiv</span> <span class="p">=</span> <span
                          class="s">{https://arxiv.org/abs/2011.01681}</span><span class="p">,</span>
                        <span class="na">code</span> <span class="p">=</span> <span
                          class="s">{https://github.com/jindongwang/transferlearning/tree/master/code/deep/CSG}</span><span
                          class="p">,</span>
                        <span class="na">pdf</span> <span class="p">=</span> <span
                          class="s">{nips21-csusalcsg.pdf}</span>
                        <span class="p">}</span></code></pre>
                  </figure>
                </div>
              </div>
              <!-- </div> -->
            </li>
            <li>
              <!-- <div class="row"> -->
              <!-- <div class="col-sm-1 abbr">
    <abbr class="badge">CIKM</abbr>
  </div> -->
              <div id="du2021adarnn" class="col-sm-8" style="max-width: 100%">
                <div
                  class="title"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Adarnn: Adaptive learning and forecasting of time series
                </div>
                <div
                  class="author"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Yuntao Du,
                  <em><b>Jindong Wang</b></em
                  ><sup>#</sup>
                  , Wenjie Feng, Sinno Pan, Tao Qin, Renjun Xu, and Chongjun
                  Wang
                </div>
                <div
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: -0.1em;
                  "
                >
                  <em
                    >The 30th ACM International Conference on Information &amp;
                    Knowledge Management (CIKM)</em
                  >
                  2021 | [
                  <a
                    href="#"
                    role="button"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >arXiv</a
                  >
                  <!-- 
        <a class="bibtex btn btn-sm z-depth-0" role="button" style="padding-top: 0em; padding-bottom: 0px;">Bib</a>
         -->
                  <a href="#" style="padding-top: 0em; padding-bottom: 0px"
                    >PDF</a
                  >
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >Code</a
                  >
                  ]
                </div>
                <!-- <div class="links" style="padding-top: 0em; padding-bottom: 0em; height: 30px; margin-bottom: -.5em;"> -->
                <!--  -->
                <!-- </div> -->
                <!-- Hidden abstract block -->
                <!-- Hidden bibtex block -->
                <div class="bibtex hidden">
                  <figure class="highlight">
                    <pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span
                          class="p">{</span><span class="nl">du2021adarnn</span><span class="p">,</span>
                        <span class="na">title</span> <span class="p">=</span> <span class="s">{Adarnn: Adaptive
                          learning and forecasting of time series}</span><span class="p">,</span>
                        <span class="na">author</span> <span class="p">=</span> <span class="s">{Du, Yuntao and Wang,
                          Jindong and Feng, Wenjie and Pan, Sinno and Qin, Tao and Xu, Renjun and Wang,
                          Chongjun}</span><span class="p">,</span>
                        <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{The 30th ACM
                          International Conference on Information \&amp; Knowledge Management (CIKM)}</span><span
                          class="p">,</span>
                        <span class="na">pages</span> <span class="p">=</span> <span class="s">{402--411}</span><span
                          class="p">,</span>
                        <span class="na">year</span> <span class="p">=</span> <span class="s">{2021}</span><span
                          class="p">,</span>
                        <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">abbr</span> <span class="p">=</span> <span class="s">{CIKM}</span><span
                          class="p">,</span>
                        <span class="na">corr</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">selected</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">arxiv</span> <span class="p">=</span> <span
                          class="s">{https://arxiv.org/abs/2108.04443}</span><span class="p">,</span>
                        <span class="na">code</span> <span class="p">=</span> <span
                          class="s">{https://github.com/jindongwang/transferlearning/tree/master/code/deep/adarnn}</span><span
                          class="p">,</span>
                        <span class="na">pdf</span> <span class="p">=</span> <span class="s">{cikm21-adarnn.pdf}</span>
                        <span class="p">}</span></code></pre>
                  </figure>
                </div>
              </div>
              <!-- </div> -->
            </li>
            <li>
              <!-- <div class="row"> -->
              <!-- <div class="col-sm-1 abbr">
    <abbr class="badge">IS</abbr>
  </div> -->
              <div id="hou2021cross" class="col-sm-8" style="max-width: 100%">
                <div
                  class="title"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Cross-domain Speech Recognition with Unsupervised
                  Character-level Distribution Matching
                </div>
                <div
                  class="author"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Wenxin Hou,
                  <em><b>Jindong Wang</b></em
                  ><sup>#</sup>
                  , Xu Tan, Tao Qin, and Takahiro Shinozaki
                </div>
                <div
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: -0.1em;
                  "
                >
                  <em>Interspeech</em>
                  2021 | [
                  <a
                    href="#"
                    role="button"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >arXiv</a
                  >
                  <!-- 
        <a class="bibtex btn btn-sm z-depth-0" role="button" style="padding-top: 0em; padding-bottom: 0px;">Bib</a>
         -->
                  <a href="#" style="padding-top: 0em; padding-bottom: 0px"
                    >PDF</a
                  >
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >Code</a
                  >
                  ]
                </div>
                <!-- <div class="links" style="padding-top: 0em; padding-bottom: 0em; height: 30px; margin-bottom: -.5em;"> -->
                <!--  -->
                <!-- </div> -->
                <!-- Hidden abstract block -->
                <!-- Hidden bibtex block -->
                <div class="bibtex hidden">
                  <figure class="highlight">
                    <pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span
                          class="p">{</span><span class="nl">hou2021cross</span><span class="p">,</span>
                        <span class="na">title</span> <span class="p">=</span> <span class="s">{Cross-domain Speech
                          Recognition with Unsupervised Character-level Distribution Matching}</span><span
                          class="p">,</span>
                        <span class="na">author</span> <span class="p">=</span> <span class="s">{Hou, Wenxin and Wang,
                          Jindong and Tan, Xu and Qin, Tao and Shinozaki, Takahiro}</span><span class="p">,</span>
                        <span class="na">booktitle</span> <span class="p">=</span> <span
                          class="s">{Interspeech}</span><span class="p">,</span>
                        <span class="na">year</span> <span class="p">=</span> <span class="s">{2021}</span><span
                          class="p">,</span>
                        <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">abbr</span> <span class="p">=</span> <span class="s">{IS}</span><span
                          class="p">,</span>
                        <span class="na">corr</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">pdf</span> <span class="p">=</span> <span
                          class="s">{a25_interspeech21.pdf}</span><span class="p">,</span>
                        <span class="na">arxiv</span> <span class="p">=</span> <span
                          class="s">{https://arxiv.org/abs/2104.07491}</span><span class="p">,</span>
                        <span class="na">code</span> <span class="p">=</span> <span
                          class="s">{https://github.com/jindongwang/transferlearning/tree/master/code/ASR/CMatch}</span>
                        <span class="p">}</span></code></pre>
                  </figure>
                </div>
              </div>
              <!-- </div> -->
            </li>
            <li>
              <!-- <div class="row"> -->
              <!-- <div class="col-sm-1 abbr">
    <abbr class="badge">IJCAI</abbr>
  </div> -->
              <div
                id="wang2021generalizing"
                class="col-sm-8"
                style="max-width: 100%"
              >
                <div
                  class="title"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Generalizing to Unseen Domains: A Survey on Domain
                  Generalization
                </div>
                <div
                  class="author"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  <em><b>Jindong Wang</b></em>
                  , Cuiling Lan, Chang Liu, Yidong Ouyang, Wenjun Zeng, and Tao
                  Qin
                </div>
                <div
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: -0.1em;
                  "
                >
                  <em
                    >International Joint Conference on Artificial Intelligence
                    (IJCAI)</em
                  >
                  2021 | [
                  <a
                    href="#"
                    role="button"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >arXiv</a
                  >
                  <!-- 
        <a class="bibtex btn btn-sm z-depth-0" role="button" style="padding-top: 0em; padding-bottom: 0px;">Bib</a>
         -->
                  <a href="#" style="padding-top: 0em; padding-bottom: 0px"
                    >PDF</a
                  >
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >Code</a
                  >
                  <a href="#" style="padding-top: 0em; padding-bottom: 0px"
                    >Slides</a
                  >
                  ]
                </div>
                <!-- <div class="links" style="padding-top: 0em; padding-bottom: 0em; height: 30px; margin-bottom: -.5em;"> -->
                <!--  -->
                <!-- </div> -->
                <!-- Hidden abstract block -->
                <!-- Hidden bibtex block -->
                <div class="bibtex hidden">
                  <figure class="highlight">
                    <pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span
                          class="p">{</span><span class="nl">wang2021generalizing</span><span class="p">,</span>
                        <span class="na">title</span> <span class="p">=</span> <span class="s">{Generalizing to Unseen
                          Domains: A Survey on Domain Generalization}</span><span class="p">,</span>
                        <span class="na">author</span> <span class="p">=</span> <span class="s">{Wang, Jindong and Lan,
                          Cuiling and Liu, Chang and Ouyang, Yidong and Zeng, Wenjun and Qin, Tao}</span><span
                          class="p">,</span>
                        <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{International Joint
                          Conference on Artificial Intelligence (IJCAI)}</span><span class="p">,</span>
                        <span class="na">year</span> <span class="p">=</span> <span class="s">{2021}</span><span
                          class="p">,</span>
                        <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">abbr</span> <span class="p">=</span> <span class="s">{IJCAI}</span><span
                          class="p">,</span>
                        <span class="na">arxiv</span> <span class="p">=</span> <span
                          class="s">{https://arxiv.org/abs/2103.03097}</span><span class="p">,</span>
                        <span class="na">code</span> <span class="p">=</span> <span
                          class="s">{https://github.com/jindongwang/transferlearning/tree/master/code/DeepDG}</span><span
                          class="p">,</span>
                        <span class="na">slides</span> <span class="p">=</span> <span
                          class="s">{DGSurvey-ppt.pdf}</span><span class="p">,</span>
                        <span class="na">pdf</span> <span class="p">=</span> <span
                          class="s">{DG_survey_TKDE22.pdf}</span>
                        <span class="p">}</span></code></pre>
                  </figure>
                </div>
              </div>
              <!-- </div> -->
            </li>
            <li>
              <!-- <div class="row"> -->
              <!-- <div class="col-sm-1 abbr">
    <abbr class="badge">ICASSP</abbr>
  </div> -->
              <div
                id="meng2021mixspeech"
                class="col-sm-8"
                style="max-width: 100%"
              >
                <div
                  class="title"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  MixSpeech: Data Augmentation for Low-resource Automatic Speech
                  Recognition
                </div>
                <div
                  class="author"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Linghui Meng, Jin Xu, Xu Tan,
                  <em><b>Jindong Wang</b></em>
                  , Tao Qin, and Bo Xu
                </div>
                <div
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: -0.1em;
                  "
                >
                  <em
                    >IEEE International Conference on Acoustics, Speech and
                    Signal Processing (ICASSP)</em
                  >
                  2021 | [
                  <a
                    href="#"
                    role="button"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >arXiv</a
                  >
                  <!-- 
        <a class="bibtex btn btn-sm z-depth-0" role="button" style="padding-top: 0em; padding-bottom: 0px;">Bib</a>
         -->
                  ]
                </div>
                <!-- <div class="links" style="padding-top: 0em; padding-bottom: 0em; height: 30px; margin-bottom: -.5em;"> -->
                <!--  -->
                <!-- </div> -->
                <!-- Hidden abstract block -->
                <!-- Hidden bibtex block -->
                <div class="bibtex hidden">
                  <figure class="highlight">
                    <pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span
                          class="p">{</span><span class="nl">meng2021mixspeech</span><span class="p">,</span>
                        <span class="na">title</span> <span class="p">=</span> <span class="s">{MixSpeech: Data
                          Augmentation for Low-resource Automatic Speech Recognition}</span><span class="p">,</span>
                        <span class="na">author</span> <span class="p">=</span> <span class="s">{Meng, Linghui and Xu,
                          Jin and Tan, Xu and Wang, Jindong and Qin, Tao and Xu, Bo}</span><span class="p">,</span>
                        <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{IEEE International
                          Conference on Acoustics, Speech and Signal Processing (ICASSP)}</span><span class="p">,</span>
                        <span class="na">pages</span> <span class="p">=</span> <span class="s">{7008--7012}</span><span
                          class="p">,</span>
                        <span class="na">year</span> <span class="p">=</span> <span class="s">{2021}</span><span
                          class="p">,</span>
                        <span class="na">organization</span> <span class="p">=</span> <span class="s">{IEEE}</span><span
                          class="p">,</span>
                        <span class="na">abbr</span> <span class="p">=</span> <span class="s">{ICASSP}</span><span
                          class="p">,</span>
                        <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">arxiv</span> <span class="p">=</span> <span
                          class="s">{https://arxiv.org/pdf/2102.12664}</span>
                        <span class="p">}</span></code></pre>
                  </figure>
                </div>
              </div>
              <!-- </div> -->
            </li>
          </ol>
          <div>2020</div>
          <ol class="bibliography">
            <li>
              <!-- <div class="row"> -->
              <!-- <div class="col-sm-1 abbr">
    <abbr class="badge">TNNLS</abbr>
  </div> -->
              <div id="zhu2020deep" class="col-sm-8" style="max-width: 100%">
                <div
                  class="title"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Deep subdomain adaptation network for image classification
                </div>
                <div
                  class="author"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Yongchun Zhu, Fuzhen Zhuang,
                  <em><b>Jindong Wang</b></em>
                  , Guolin Ke, Jingwu Chen, Jiang Bian, Hui Xiong, and Qing He
                </div>
                <div
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: -0.1em;
                  "
                >
                  <em
                    >IEEE transactions on neural networks and learning
                    systems</em
                  >
                  2020 | [
                  <!-- 
        <a class="bibtex btn btn-sm z-depth-0" role="button" style="padding-top: 0em; padding-bottom: 0px;">Bib</a>
         -->
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >HTML</a
                  >
                  <a href="#" style="padding-top: 0em; padding-bottom: 0px"
                    >PDF</a
                  >
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >Code</a
                  >
                  ]
                </div>
                <!-- <div class="links" style="padding-top: 0em; padding-bottom: 0em; height: 30px; margin-bottom: -.5em;"> -->
                <!--  -->
                <!-- </div> -->
                <!-- Hidden abstract block -->
                <!-- Hidden bibtex block -->
                <div class="bibtex hidden">
                  <figure class="highlight">
                    <pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span
                          class="p">{</span><span class="nl">zhu2020deep</span><span class="p">,</span>
                        <span class="na">title</span> <span class="p">=</span> <span class="s">{Deep subdomain
                          adaptation network for image classification}</span><span class="p">,</span>
                        <span class="na">author</span> <span class="p">=</span> <span class="s">{Zhu, Yongchun and
                          Zhuang, Fuzhen and Wang, Jindong and Ke, Guolin and Chen, Jingwu and Bian, Jiang and Xiong,
                          Hui and He, Qing}</span><span class="p">,</span>
                        <span class="na">journal</span> <span class="p">=</span> <span class="s">{IEEE transactions on
                          neural networks and learning systems}</span><span class="p">,</span>
                        <span class="na">volume</span> <span class="p">=</span> <span class="s">{32}</span><span
                          class="p">,</span>
                        <span class="na">number</span> <span class="p">=</span> <span class="s">{4}</span><span
                          class="p">,</span>
                        <span class="na">pages</span> <span class="p">=</span> <span class="s">{1713--1722}</span><span
                          class="p">,</span>
                        <span class="na">year</span> <span class="p">=</span> <span class="s">{2020}</span><span
                          class="p">,</span>
                        <span class="na">publisher</span> <span class="p">=</span> <span class="s">{IEEE}</span><span
                          class="p">,</span>
                        <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">pdf</span> <span class="p">=</span> <span
                          class="s">{a24tnnls20.pdf}</span><span class="p">,</span>
                        <span class="na">html</span> <span class="p">=</span> <span
                          class="s">{https://ieeexplore.ieee.org/document/9085896}</span><span class="p">,</span>
                        <span class="na">code</span> <span class="p">=</span> <span
                          class="s">{https://github.com/jindongwang/transferlearning/tree/master/code/DeepDA}</span><span
                          class="p">,</span>
                        <span class="na">abbr</span> <span class="p">=</span> <span class="s">{TNNLS}</span>
                        <span class="p">}</span></code></pre>
                  </figure>
                </div>
              </div>
              <!-- </div> -->
            </li>
            <li>
              <!-- <div class="row"> -->
              <!-- <div class="col-sm-1 abbr">
  </div> -->
              <div
                id="chen2020fedhealth"
                class="col-sm-8"
                style="max-width: 100%"
              >
                <div
                  class="title"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Fedhealth: A federated transfer learning framework for
                  wearable healthcare
                </div>
                <div
                  class="author"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Yiqiang Chen, Xin Qin,
                  <em><b>Jindong Wang</b></em>
                  , Chaohui Yu, and Wen Gao
                </div>
                <div
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: -0.1em;
                  "
                >
                  <em>IEEE Intelligent Systems</em>
                  2020 | [
                  <!-- 
        <a class="bibtex btn btn-sm z-depth-0" role="button" style="padding-top: 0em; padding-bottom: 0px;">Bib</a>
         -->
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >HTML</a
                  >
                  <a href="#" style="padding-top: 0em; padding-bottom: 0px"
                    >PDF</a
                  >
                  ]
                </div>
                <!-- <div class="links" style="padding-top: 0em; padding-bottom: 0em; height: 30px; margin-bottom: -.5em;"> -->
                <!--  -->
                <!-- </div> -->
                <!-- Hidden abstract block -->
                <!-- Hidden bibtex block -->
                <div class="bibtex hidden">
                  <figure class="highlight">
                    <pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span
                          class="p">{</span><span class="nl">chen2020fedhealth</span><span class="p">,</span>
                        <span class="na">title</span> <span class="p">=</span> <span class="s">{Fedhealth: A federated
                          transfer learning framework for wearable healthcare}</span><span class="p">,</span>
                        <span class="na">author</span> <span class="p">=</span> <span class="s">{Chen, Yiqiang and Qin,
                          Xin and Wang, Jindong and Yu, Chaohui and Gao, Wen}</span><span class="p">,</span>
                        <span class="na">journal</span> <span class="p">=</span> <span class="s">{IEEE Intelligent
                          Systems}</span><span class="p">,</span>
                        <span class="na">volume</span> <span class="p">=</span> <span class="s">{35}</span><span
                          class="p">,</span>
                        <span class="na">number</span> <span class="p">=</span> <span class="s">{4}</span><span
                          class="p">,</span>
                        <span class="na">pages</span> <span class="p">=</span> <span class="s">{83--93}</span><span
                          class="p">,</span>
                        <span class="na">year</span> <span class="p">=</span> <span class="s">{2020}</span><span
                          class="p">,</span>
                        <span class="na">publisher</span> <span class="p">=</span> <span class="s">{IEEE}</span><span
                          class="p">,</span>
                        <span class="na">pdf</span> <span class="p">=</span> <span
                          class="s">{a21_intesys20.pdf}</span><span class="p">,</span>
                        <span class="na">html</span> <span class="p">=</span> <span
                          class="s">{https://ieeexplore.ieee.org/document/9076082}</span><span class="p">,</span>
                        <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span>
                        <span class="p">}</span></code></pre>
                  </figure>
                </div>
              </div>
              <!-- </div> -->
            </li>
            <li>
              <!-- <div class="row"> -->
              <!-- <div class="col-sm-1 abbr">
    <abbr class="badge">TIST</abbr>
  </div> -->
              <div
                id="wang2020transfer"
                class="col-sm-8"
                style="max-width: 100%"
              >
                <div
                  class="title"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Transfer learning with dynamic distribution adaptation
                </div>
                <div
                  class="author"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  <em><b>Jindong Wang</b></em>
                  , Yiqiang Chen, Wenjie Feng, Han Yu, Meiyu Huang, and Qiang
                  Yang
                </div>
                <div
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: -0.1em;
                  "
                >
                  <em
                    >ACM Transactions on Intelligent Systems and Technology
                    (TIST)</em
                  >
                  2020 | [
                  <!-- 
        <a class="bibtex btn btn-sm z-depth-0" role="button" style="padding-top: 0em; padding-bottom: 0px;">Bib</a>
         -->
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >HTML</a
                  >
                  <a href="#" style="padding-top: 0em; padding-bottom: 0px"
                    >PDF</a
                  >
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >Code</a
                  >
                  ]
                </div>
                <!-- <div class="links" style="padding-top: 0em; padding-bottom: 0em; height: 30px; margin-bottom: -.5em;"> -->
                <!--  -->
                <!-- </div> -->
                <!-- Hidden abstract block -->
                <!-- Hidden bibtex block -->
                <div class="bibtex hidden">
                  <figure class="highlight">
                    <pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span
                          class="p">{</span><span class="nl">wang2020transfer</span><span class="p">,</span>
                        <span class="na">title</span> <span class="p">=</span> <span class="s">{Transfer learning with
                          dynamic distribution adaptation}</span><span class="p">,</span>
                        <span class="na">author</span> <span class="p">=</span> <span class="s">{Wang, Jindong and Chen,
                          Yiqiang and Feng, Wenjie and Yu, Han and Huang, Meiyu and Yang, Qiang}</span><span
                          class="p">,</span>
                        <span class="na">journal</span> <span class="p">=</span> <span class="s">{ACM Transactions on
                          Intelligent Systems and Technology (TIST)}</span><span class="p">,</span>
                        <span class="na">volume</span> <span class="p">=</span> <span class="s">{11}</span><span
                          class="p">,</span>
                        <span class="na">number</span> <span class="p">=</span> <span class="s">{1}</span><span
                          class="p">,</span>
                        <span class="na">pages</span> <span class="p">=</span> <span class="s">{1--25}</span><span
                          class="p">,</span>
                        <span class="na">year</span> <span class="p">=</span> <span class="s">{2020}</span><span
                          class="p">,</span>
                        <span class="na">publisher</span> <span class="p">=</span> <span class="s">{ACM New York, NY,
                          USA}</span><span class="p">,</span>
                        <span class="na">pdf</span> <span class="p">=</span> <span
                          class="s">{a17_tist19.pdf}</span><span class="p">,</span>
                        <span class="na">html</span> <span class="p">=</span> <span
                          class="s">{https://dl.acm.org/doi/abs/10.1145/3360309}</span><span class="p">,</span>
                        <span class="na">code</span> <span class="p">=</span> <span
                          class="s">{https://github.com/jindongwang/transferlearning/tree/master/code/deep/DeepMEDA}</span><span
                          class="p">,</span>
                        <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">abbr</span> <span class="p">=</span> <span class="s">{TIST}</span>
                        <span class="p">}</span></code></pre>
                  </figure>
                </div>
              </div>
              <!-- </div> -->
            </li>
            <li>
              <!-- <div class="row"> -->
              <!-- <div class="col-sm-1 abbr">
    <abbr class="badge">IJCAI</abbr>
  </div> -->
              <div id="xu2020joint" class="col-sm-8" style="max-width: 100%">
                <div
                  class="title"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Joint Partial Optimal Transport for Open Set Domain
                  Adaptation.
                </div>
                <div
                  class="author"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Renjun Xu, Pelen Liu, Yin Zhang, Fang Cai,
                  <em><b>Jindong Wang</b></em>
                  , Shuoying Liang, Heting Ying, and Jianwei Yin
                </div>
                <div
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: -0.1em;
                  "
                >
                  <em
                    >International Joint Conference on Artificial Intelligence
                    (IJCAI)</em
                  >
                  2020 | [
                  <!-- 
        <a class="bibtex btn btn-sm z-depth-0" role="button" style="padding-top: 0em; padding-bottom: 0px;">Bib</a>
         -->
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >HTML</a
                  >
                  <a href="#" style="padding-top: 0em; padding-bottom: 0px"
                    >PDF</a
                  >
                  ]
                </div>
                <!-- <div class="links" style="padding-top: 0em; padding-bottom: 0em; height: 30px; margin-bottom: -.5em;"> -->
                <!--  -->
                <!-- </div> -->
                <!-- Hidden abstract block -->
                <!-- Hidden bibtex block -->
                <div class="bibtex hidden">
                  <figure class="highlight">
                    <pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span
                          class="p">{</span><span class="nl">xu2020joint</span><span class="p">,</span>
                        <span class="na">title</span> <span class="p">=</span> <span class="s">{Joint Partial Optimal
                          Transport for Open Set Domain Adaptation.}</span><span class="p">,</span>
                        <span class="na">author</span> <span class="p">=</span> <span class="s">{Xu, Renjun and Liu,
                          Pelen and Zhang, Yin and Cai, Fang and Wang, Jindong and Liang, Shuoying and Ying, Heting and
                          Yin, Jianwei}</span><span class="p">,</span>
                        <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{International Joint
                          Conference on Artificial Intelligence (IJCAI)}</span><span class="p">,</span>
                        <span class="na">pages</span> <span class="p">=</span> <span class="s">{2540--2546}</span><span
                          class="p">,</span>
                        <span class="na">year</span> <span class="p">=</span> <span class="s">{2020}</span><span
                          class="p">,</span>
                        <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">abbr</span> <span class="p">=</span> <span class="s">{IJCAI}</span><span
                          class="p">,</span>
                        <span class="na">html</span> <span class="p">=</span> <span
                          class="s">{https://www.ijcai.org/Proceedings/2020/352}</span><span class="p">,</span>
                        <span class="na">pdf</span> <span class="p">=</span> <span class="s">{a23_ijcai20.pdf}</span>
                        <span class="p">}</span></code></pre>
                  </figure>
                </div>
              </div>
              <!-- </div> -->
            </li>
            <li>
              <!-- <div class="row"> -->
              <!-- <div class="col-sm-1 abbr">
    <abbr class="badge">CVPR</abbr>
  </div> -->
              <div id="xu2020reliable" class="col-sm-8" style="max-width: 100%">
                <div
                  class="title"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Reliable weighted optimal transport for unsupervised domain
                  adaptation
                </div>
                <div
                  class="author"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Renjun Xu, Pelen Liu, Liyan Wang, Chao Chen, and
                  <em><b>Jindong Wang#</b></em>
                </div>
                <div
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: -0.1em;
                  "
                >
                  <em
                    >Proceedings of the IEEE/CVF Conference on Computer Vision
                    and Pattern Recognition (CVPR)</em
                  >
                  2020 | [
                  <!-- 
        <a class="bibtex btn btn-sm z-depth-0" role="button" style="padding-top: 0em; padding-bottom: 0px;">Bib</a>
         -->
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >HTML</a
                  >
                  <a href="#" style="padding-top: 0em; padding-bottom: 0px"
                    >PDF</a
                  >
                  ]
                </div>
                <!-- <div class="links" style="padding-top: 0em; padding-bottom: 0em; height: 30px; margin-bottom: -.5em;"> -->
                <!--  -->
                <!-- </div> -->
                <!-- Hidden abstract block -->
                <!-- Hidden bibtex block -->
                <div class="bibtex hidden">
                  <figure class="highlight">
                    <pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span
                          class="p">{</span><span class="nl">xu2020reliable</span><span class="p">,</span>
                        <span class="na">title</span> <span class="p">=</span> <span class="s">{Reliable weighted
                          optimal transport for unsupervised domain adaptation}</span><span class="p">,</span>
                        <span class="na">author</span> <span class="p">=</span> <span class="s">{Xu, Renjun and Liu,
                          Pelen and Wang, Liyan and Chen, Chao and Wang, Jindong}</span><span class="p">,</span>
                        <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{Proceedings of the
                          IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}</span><span
                          class="p">,</span>
                        <span class="na">pages</span> <span class="p">=</span> <span class="s">{4394--4403}</span><span
                          class="p">,</span>
                        <span class="na">year</span> <span class="p">=</span> <span class="s">{2020}</span><span
                          class="p">,</span>
                        <span class="na">abbr</span> <span class="p">=</span> <span class="s">{CVPR}</span><span
                          class="p">,</span>
                        <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">html</span> <span class="p">=</span> <span
                          class="s">{http://openaccess.thecvf.com/content_CVPR_2020/html/Xu_Reliable_Weighted_Optimal_Transport_for_Unsupervised_Domain_Adaptation_CVPR_2020_paper.html}</span><span
                          class="p">,</span>
                        <span class="na">pdf</span> <span class="p">=</span> <span class="s">{a22_cvpr20.pdf}</span>
                        <span class="p">}</span></code></pre>
                  </figure>
                </div>
              </div>
              <!-- </div> -->
            </li>
          </ol>
          <div>2019</div>
          <ol class="bibliography">
            <li>
              <!-- <div class="row"> -->
              <!-- <div class="col-sm-1 abbr">
    <abbr class="badge">IMWUT</abbr>
  </div> -->
              <div id="qin2019cross" class="col-sm-8" style="max-width: 100%">
                <div
                  class="title"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Cross-dataset activity recognition via adaptive
                  spatial-temporal transfer learning
                </div>
                <div
                  class="author"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Xin Qin, Yiqiang Chen,
                  <em><b>Jindong Wang</b></em>
                  , and Chaohui Yu
                </div>
                <div
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: -0.1em;
                  "
                >
                  <em
                    >Proceedings of the ACM on Interactive, Mobile, Wearable and
                    Ubiquitous Technologies (IMWUT)</em
                  >
                  2019 | [
                  <!-- 
        <a class="bibtex btn btn-sm z-depth-0" role="button" style="padding-top: 0em; padding-bottom: 0px;">Bib</a>
         -->
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >HTML</a
                  >
                  <a href="#" style="padding-top: 0em; padding-bottom: 0px"
                    >PDF</a
                  >
                  ]
                </div>
                <!-- <div class="links" style="padding-top: 0em; padding-bottom: 0em; height: 30px; margin-bottom: -.5em;"> -->
                <!--  -->
                <!-- </div> -->
                <!-- Hidden abstract block -->
                <!-- Hidden bibtex block -->
                <div class="bibtex hidden">
                  <figure class="highlight">
                    <pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span
                          class="p">{</span><span class="nl">qin2019cross</span><span class="p">,</span>
                        <span class="na">title</span> <span class="p">=</span> <span class="s">{Cross-dataset activity
                          recognition via adaptive spatial-temporal transfer learning}</span><span class="p">,</span>
                        <span class="na">author</span> <span class="p">=</span> <span class="s">{Qin, Xin and Chen,
                          Yiqiang and Wang, Jindong and Yu, Chaohui}</span><span class="p">,</span>
                        <span class="na">journal</span> <span class="p">=</span> <span class="s">{Proceedings of the ACM
                          on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT)}</span><span
                          class="p">,</span>
                        <span class="na">volume</span> <span class="p">=</span> <span class="s">{3}</span><span
                          class="p">,</span>
                        <span class="na">number</span> <span class="p">=</span> <span class="s">{4}</span><span
                          class="p">,</span>
                        <span class="na">pages</span> <span class="p">=</span> <span class="s">{1--25}</span><span
                          class="p">,</span>
                        <span class="na">year</span> <span class="p">=</span> <span class="s">{2019}</span><span
                          class="p">,</span>
                        <span class="na">publisher</span> <span class="p">=</span> <span class="s">{ACM New York, NY,
                          USA}</span><span class="p">,</span>
                        <span class="na">pdf</span> <span class="p">=</span> <span
                          class="s">{a20_ubicomp20.pdf}</span><span class="p">,</span>
                        <span class="na">html</span> <span class="p">=</span> <span
                          class="s">{https://dl.acm.org/doi/abs/10.1145/3369818}</span><span class="p">,</span>
                        <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">abbr</span> <span class="p">=</span> <span class="s">{IMWUT}</span>
                        <span class="p">}</span></code></pre>
                  </figure>
                </div>
              </div>
              <!-- </div> -->
            </li>
            <li>
              <!-- <div class="row"> -->
              <!-- <div class="col-sm-1 abbr">
    <abbr class="badge">JMLC</abbr>
  </div> -->
              <div id="yu2019transfer" class="col-sm-8" style="max-width: 100%">
                <div
                  class="title"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Transfer channel pruning for compressing deep domain
                  adaptation models
                </div>
                <div
                  class="author"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Chaohui Yu,
                  <em><b>Jindong Wang</b></em>
                  , Yiqiang Chen, and Xin Qin
                </div>
                <div
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: -0.1em;
                  "
                >
                  <em
                    >International Journal of Machine Learning and Cybernetics
                    (JMLC)</em
                  >
                  2019 | [
                  <!-- 
        <a class="bibtex btn btn-sm z-depth-0" role="button" style="padding-top: 0em; padding-bottom: 0px;">Bib</a>
         -->
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >HTML</a
                  >
                  <a href="#" style="padding-top: 0em; padding-bottom: 0px"
                    >PDF</a
                  >
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >Code</a
                  >
                  ]
                </div>
                <!-- <div class="links" style="padding-top: 0em; padding-bottom: 0em; height: 30px; margin-bottom: -.5em;"> -->
                <!--  -->
                <!-- </div> -->
                <!-- Hidden abstract block -->
                <!-- Hidden bibtex block -->
                <div class="bibtex hidden">
                  <figure class="highlight">
                    <pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span
                          class="p">{</span><span class="nl">yu2019transfer</span><span class="p">,</span>
                        <span class="na">title</span> <span class="p">=</span> <span class="s">{Transfer channel pruning
                          for compressing deep domain adaptation models}</span><span class="p">,</span>
                        <span class="na">author</span> <span class="p">=</span> <span class="s">{Yu, Chaohui and Wang,
                          Jindong and Chen, Yiqiang and Qin, Xin}</span><span class="p">,</span>
                        <span class="na">journal</span> <span class="p">=</span> <span class="s">{International Journal
                          of Machine Learning and Cybernetics (JMLC)}</span><span class="p">,</span>
                        <span class="na">volume</span> <span class="p">=</span> <span class="s">{10}</span><span
                          class="p">,</span>
                        <span class="na">number</span> <span class="p">=</span> <span class="s">{11}</span><span
                          class="p">,</span>
                        <span class="na">pages</span> <span class="p">=</span> <span class="s">{3129--3144}</span><span
                          class="p">,</span>
                        <span class="na">year</span> <span class="p">=</span> <span class="s">{2019}</span><span
                          class="p">,</span>
                        <span class="na">publisher</span> <span class="p">=</span> <span
                          class="s">{Springer}</span><span class="p">,</span>
                        <span class="na">html</span> <span class="p">=</span> <span
                          class="s">{http://link.springer.com/article/10.1007/s13042-019-01004-6}</span><span
                          class="p">,</span>
                        <span class="na">pdf</span> <span class="p">=</span> <span
                          class="s">{a18_ijmlc19.pdf}</span><span class="p">,</span>
                        <span class="na">code</span> <span class="p">=</span> <span
                          class="s">{https://github.com/jindongwang/transferlearning/tree/master/code/deep/TCP}</span><span
                          class="p">,</span>
                        <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">abbr</span> <span class="p">=</span> <span class="s">{JMLC}</span>
                        <span class="p">}</span></code></pre>
                  </figure>
                </div>
              </div>
              <!-- </div> -->
            </li>
            <li>
              <!-- <div class="row"> -->
              <!-- <div class="col-sm-1 abbr">
    <abbr class="badge">NeuNet</abbr>
  </div> -->
              <div id="zhu2019multi" class="col-sm-8" style="max-width: 100%">
                <div
                  class="title"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Multi-representation adaptation network for cross-domain image
                  classification
                </div>
                <div
                  class="author"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Yongchun Zhu, Fuzhen Zhuang,
                  <em><b>Jindong Wang</b></em>
                  , Jingwu Chen, Zhiping Shi, Wenjuan Wu, and Qing He
                </div>
                <div
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: -0.1em;
                  "
                >
                  <em>Neural Networks</em>
                  2019 | [
                  <!-- 
        <a class="bibtex btn btn-sm z-depth-0" role="button" style="padding-top: 0em; padding-bottom: 0px;">Bib</a>
         -->
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >HTML</a
                  >
                  <a href="#" style="padding-top: 0em; padding-bottom: 0px"
                    >PDF</a
                  >
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >Code</a
                  >
                  ]
                </div>
                <!-- <div class="links" style="padding-top: 0em; padding-bottom: 0em; height: 30px; margin-bottom: -.5em;"> -->
                <!--  -->
                <!-- </div> -->
                <!-- Hidden abstract block -->
                <!-- Hidden bibtex block -->
                <div class="bibtex hidden">
                  <figure class="highlight">
                    <pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span
                          class="p">{</span><span class="nl">zhu2019multi</span><span class="p">,</span>
                        <span class="na">title</span> <span class="p">=</span> <span class="s">{Multi-representation
                          adaptation network for cross-domain image classification}</span><span class="p">,</span>
                        <span class="na">author</span> <span class="p">=</span> <span class="s">{Zhu, Yongchun and
                          Zhuang, Fuzhen and Wang, Jindong and Chen, Jingwu and Shi, Zhiping and Wu, Wenjuan and He,
                          Qing}</span><span class="p">,</span>
                        <span class="na">journal</span> <span class="p">=</span> <span class="s">{Neural
                          Networks}</span><span class="p">,</span>
                        <span class="na">volume</span> <span class="p">=</span> <span class="s">{119}</span><span
                          class="p">,</span>
                        <span class="na">pages</span> <span class="p">=</span> <span class="s">{214--221}</span><span
                          class="p">,</span>
                        <span class="na">year</span> <span class="p">=</span> <span class="s">{2019}</span><span
                          class="p">,</span>
                        <span class="na">publisher</span> <span class="p">=</span> <span
                          class="s">{Elsevier}</span><span class="p">,</span>
                        <span class="na">html</span> <span class="p">=</span> <span
                          class="s">{https://doi.org/10.1016/j.neunet.2019.07.010}</span><span class="p">,</span>
                        <span class="na">pdf</span> <span class="p">=</span> <span
                          class="s">{a19_neunet19.pdf}</span><span class="p">,</span>
                        <span class="na">code</span> <span class="p">=</span> <span
                          class="s">{https://github.com/jindongwang/transferlearning/tree/master/code/deep/MRAN}</span><span
                          class="p">,</span>
                        <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">abbr</span> <span class="p">=</span> <span class="s">{NeuNet}</span>
                        <span class="p">}</span></code></pre>
                  </figure>
                </div>
              </div>
              <!-- </div> -->
            </li>
            <li>
              <!-- <div class="row"> -->
              <!-- <div class="col-sm-1 abbr">
    <abbr class="badge">PMC</abbr>
  </div> -->
              <div id="chen2019cross" class="col-sm-8" style="max-width: 100%">
                <div
                  class="title"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Cross-position activity recognition with stratified transfer
                  learning
                </div>
                <div
                  class="author"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Yiqiang Chen,
                  <em><b>Jindong Wang</b></em>
                  , Meiyu Huang, and Han Yu
                </div>
                <div
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: -0.1em;
                  "
                >
                  <em>Pervasive and Mobile Computing</em>
                  2019 | [
                  <!-- 
        <a class="bibtex btn btn-sm z-depth-0" role="button" style="padding-top: 0em; padding-bottom: 0px;">Bib</a>
         -->
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >HTML</a
                  >
                  <a href="#" style="padding-top: 0em; padding-bottom: 0px"
                    >PDF</a
                  >
                  ]
                </div>
                <!-- <div class="links" style="padding-top: 0em; padding-bottom: 0em; height: 30px; margin-bottom: -.5em;"> -->
                <!--  -->
                <!-- </div> -->
                <!-- Hidden abstract block -->
                <!-- Hidden bibtex block -->
                <div class="bibtex hidden">
                  <figure class="highlight">
                    <pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span
                          class="p">{</span><span class="nl">chen2019cross</span><span class="p">,</span>
                        <span class="na">title</span> <span class="p">=</span> <span class="s">{Cross-position activity
                          recognition with stratified transfer learning}</span><span class="p">,</span>
                        <span class="na">author</span> <span class="p">=</span> <span class="s">{Chen, Yiqiang and Wang,
                          Jindong and Huang, Meiyu and Yu, Han}</span><span class="p">,</span>
                        <span class="na">journal</span> <span class="p">=</span> <span class="s">{Pervasive and Mobile
                          Computing}</span><span class="p">,</span>
                        <span class="na">volume</span> <span class="p">=</span> <span class="s">{57}</span><span
                          class="p">,</span>
                        <span class="na">pages</span> <span class="p">=</span> <span class="s">{1--13}</span><span
                          class="p">,</span>
                        <span class="na">year</span> <span class="p">=</span> <span class="s">{2019}</span><span
                          class="p">,</span>
                        <span class="na">publisher</span> <span class="p">=</span> <span
                          class="s">{Elsevier}</span><span class="p">,</span>
                        <span class="na">html</span> <span class="p">=</span> <span
                          class="s">{https://doi.org/10.1016/j.pmcj.2019.04.004}</span><span class="p">,</span>
                        <span class="na">pdf</span> <span class="p">=</span> <span class="s">{a14_pmc19.pdf}</span><span
                          class="p">,</span>
                        <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">abbr</span> <span class="p">=</span> <span class="s">{PMC}</span>
                        <span class="p">}</span></code></pre>
                  </figure>
                </div>
              </div>
              <!-- </div> -->
            </li>
            <li>
              <!-- <div class="row"> -->
              <!-- <div class="col-sm-1 abbr">
    <abbr class="badge">PRL</abbr>
  </div> -->
              <div id="wang2019deep" class="col-sm-8" style="max-width: 100%">
                <div
                  class="title"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Deep learning for sensor-based activity recognition: A survey
                </div>
                <div
                  class="author"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  <em><b>Jindong Wang</b></em>
                  , Yiqiang Chen, Shuji Hao, Xiaohui Peng, and Lisha Hu
                </div>
                <div
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: -0.1em;
                  "
                >
                  <em>Pattern Recognition Letters</em>
                  2019 | [
                  <!-- 
        <a class="bibtex btn btn-sm z-depth-0" role="button" style="padding-top: 0em; padding-bottom: 0px;">Bib</a>
         -->
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >HTML</a
                  >
                  <a href="#" style="padding-top: 0em; padding-bottom: 0px"
                    >PDF</a
                  >
                  ]
                </div>
                <!-- <div class="links" style="padding-top: 0em; padding-bottom: 0em; height: 30px; margin-bottom: -.5em;"> -->
                <!--  -->
                <!-- </div> -->
                <!-- Hidden abstract block -->
                <!-- Hidden bibtex block -->
                <div class="bibtex hidden">
                  <figure class="highlight">
                    <pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span
                          class="p">{</span><span class="nl">wang2019deep</span><span class="p">,</span>
                        <span class="na">title</span> <span class="p">=</span> <span class="s">{Deep learning for
                          sensor-based activity recognition: A survey}</span><span class="p">,</span>
                        <span class="na">author</span> <span class="p">=</span> <span class="s">{Wang, Jindong and Chen,
                          Yiqiang and Hao, Shuji and Peng, Xiaohui and Hu, Lisha}</span><span class="p">,</span>
                        <span class="na">journal</span> <span class="p">=</span> <span class="s">{Pattern Recognition
                          Letters}</span><span class="p">,</span>
                        <span class="na">volume</span> <span class="p">=</span> <span class="s">{119}</span><span
                          class="p">,</span>
                        <span class="na">pages</span> <span class="p">=</span> <span class="s">{3--11}</span><span
                          class="p">,</span>
                        <span class="na">year</span> <span class="p">=</span> <span class="s">{2019}</span><span
                          class="p">,</span>
                        <span class="na">publisher</span> <span class="p">=</span> <span
                          class="s">{Elsevier}</span><span class="p">,</span>
                        <span class="na">html</span> <span class="p">=</span> <span
                          class="s">{https://www.sciencedirect.com/science/article/pii/S016786551830045X}</span><span
                          class="p">,</span>
                        <span class="na">pdf</span> <span class="p">=</span> <span class="s">{a10_prl18.pdf}</span><span
                          class="p">,</span>
                        <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">abbr</span> <span class="p">=</span> <span class="s">{PRL}</span>
                        <span class="p">}</span></code></pre>
                  </figure>
                </div>
              </div>
              <!-- </div> -->
            </li>
            <li>
              <!-- <div class="row"> -->
              <!-- <div class="col-sm-1 abbr">
    <abbr class="badge">ICDM</abbr>
  </div> -->
              <div id="yu2019transfes" class="col-sm-8" style="max-width: 100%">
                <div
                  class="title"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Transfer learning with dynamic adversarial adaptation network
                </div>
                <div
                  class="author"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Chaohui Yu,
                  <em><b>Jindong Wang</b></em>
                  , Yiqiang Chen, and Meiyu Huang
                </div>
                <div
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: -0.1em;
                  "
                >
                  <em
                    >2019 IEEE International Conference on Data Mining
                    (ICDM)</em
                  >
                  2019 | [
                  <!-- 
        <a class="bibtex btn btn-sm z-depth-0" role="button" style="padding-top: 0em; padding-bottom: 0px;">Bib</a>
         -->
                  <a href="#" style="padding-top: 0em; padding-bottom: 0px"
                    >PDF</a
                  >
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >Code</a
                  >
                  ]
                </div>
                <!-- <div class="links" style="padding-top: 0em; padding-bottom: 0em; height: 30px; margin-bottom: -.5em;"> -->
                <!--  -->
                <!-- </div> -->
                <!-- Hidden abstract block -->
                <!-- Hidden bibtex block -->
                <div class="bibtex hidden">
                  <figure class="highlight">
                    <pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span
                          class="p">{</span><span class="nl">yu2019transfes</span><span class="p">,</span>
                        <span class="na">title</span> <span class="p">=</span> <span class="s">{Transfer learning with
                          dynamic adversarial adaptation network}</span><span class="p">,</span>
                        <span class="na">author</span> <span class="p">=</span> <span class="s">{Yu, Chaohui and Wang,
                          Jindong and Chen, Yiqiang and Huang, Meiyu}</span><span class="p">,</span>
                        <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{2019 IEEE
                          International Conference on Data Mining (ICDM)}</span><span class="p">,</span>
                        <span class="na">pages</span> <span class="p">=</span> <span class="s">{778--786}</span><span
                          class="p">,</span>
                        <span class="na">year</span> <span class="p">=</span> <span class="s">{2019}</span><span
                          class="p">,</span>
                        <span class="na">organization</span> <span class="p">=</span> <span class="s">{IEEE}</span><span
                          class="p">,</span>
                        <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">abbr</span> <span class="p">=</span> <span class="s">{ICDM}</span><span
                          class="p">,</span>
                        <span class="na">code</span> <span class="p">=</span> <span
                          class="s">{https://github.com/jindongwang/transferlearning/tree/master/code/deep/DAAN}</span><span
                          class="p">,</span>
                        <span class="na">pdf</span> <span class="p">=</span> <span class="s">{a16_icdm19.pdf}</span>
                        <span class="p">}</span></code></pre>
                  </figure>
                </div>
              </div>
              <!-- </div> -->
            </li>
            <li>
              <!-- <div class="row"> -->
              <!-- <div class="col-sm-1 abbr">
    <abbr class="badge">UIC</abbr>
  </div> -->
              <div
                id="yu2019drowsydet"
                class="col-sm-8"
                style="max-width: 100%"
              >
                <div
                  class="title"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  DrowsyDet: A Mobile Application for Real-time Driver
                  Drowsiness Detection
                </div>
                <div
                  class="author"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Chaohui Yu, Xin Qin, Yiqiang Chen,
                  <em><b>Jindong Wang</b></em>
                  , and Chenchen Fan
                </div>
                <div
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: -0.1em;
                  "
                >
                  <em>Ubiquitous Intelligence Computing</em>
                  2019 | [
                  <!-- 
        <a class="bibtex btn btn-sm z-depth-0" role="button" style="padding-top: 0em; padding-bottom: 0px;">Bib</a>
         -->
                  ]
                </div>
                <!-- <div class="links" style="padding-top: 0em; padding-bottom: 0em; height: 30px; margin-bottom: -.5em;"> -->
                <!--  -->
                <!-- </div> -->
                <!-- Hidden abstract block -->
                <!-- Hidden bibtex block -->
                <div class="bibtex hidden">
                  <figure class="highlight">
                    <pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span
                          class="p">{</span><span class="nl">yu2019drowsydet</span><span class="p">,</span>
                        <span class="na">title</span> <span class="p">=</span> <span class="s">{DrowsyDet: A Mobile
                          Application for Real-time Driver Drowsiness Detection}</span><span class="p">,</span>
                        <span class="na">author</span> <span class="p">=</span> <span class="s">{Yu, Chaohui and Qin,
                          Xin and Chen, Yiqiang and Wang, Jindong and Fan, Chenchen}</span><span class="p">,</span>
                        <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{Ubiquitous
                          Intelligence Computing}</span><span class="p">,</span>
                        <span class="na">pages</span> <span class="p">=</span> <span class="s">{425--432}</span><span
                          class="p">,</span>
                        <span class="na">year</span> <span class="p">=</span> <span class="s">{2019}</span><span
                          class="p">,</span>
                        <span class="na">organization</span> <span class="p">=</span> <span class="s">{IEEE}</span><span
                          class="p">,</span>
                        <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">abbr</span> <span class="p">=</span> <span class="s">{UIC}</span>
                        <span class="p">}</span></code></pre>
                  </figure>
                </div>
              </div>
              <!-- </div> -->
            </li>
            <li>
              <!-- <div class="row"> -->
              <!-- <div class="col-sm-1 abbr">
    <abbr class="badge">ICME</abbr>
  </div> -->
              <div id="wang2019easy" class="col-sm-8" style="max-width: 100%">
                <div
                  class="title"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Easy transfer learning by exploiting intra-domain structures
                </div>
                <div
                  class="author"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  <em><b>Jindong Wang</b></em>
                  , Yiqiang Chen, Han Yu, Meiyu Huang, and Qiang Yang
                </div>
                <div
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: -0.1em;
                  "
                >
                  <em
                    >2019 IEEE International Conference on Multimedia and Expo
                    (ICME)</em
                  >
                  2019 | [
                  <!-- 
        <a class="bibtex btn btn-sm z-depth-0" role="button" style="padding-top: 0em; padding-bottom: 0px;">Bib</a>
         -->
                  <a href="#" style="padding-top: 0em; padding-bottom: 0px"
                    >PDF</a
                  >
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >Code</a
                  >
                  ]
                </div>
                <!-- <div class="links" style="padding-top: 0em; padding-bottom: 0em; height: 30px; margin-bottom: -.5em;"> -->
                <!--  -->
                <!-- </div> -->
                <!-- Hidden abstract block -->
                <!-- Hidden bibtex block -->
                <div class="bibtex hidden">
                  <figure class="highlight">
                    <pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span
                          class="p">{</span><span class="nl">wang2019easy</span><span class="p">,</span>
                        <span class="na">title</span> <span class="p">=</span> <span class="s">{Easy transfer learning
                          by exploiting intra-domain structures}</span><span class="p">,</span>
                        <span class="na">author</span> <span class="p">=</span> <span class="s">{Wang, Jindong and Chen,
                          Yiqiang and Yu, Han and Huang, Meiyu and Yang, Qiang}</span><span class="p">,</span>
                        <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{2019 IEEE
                          International Conference on Multimedia and Expo (ICME)}</span><span class="p">,</span>
                        <span class="na">pages</span> <span class="p">=</span> <span class="s">{1210--1215}</span><span
                          class="p">,</span>
                        <span class="na">year</span> <span class="p">=</span> <span class="s">{2019}</span><span
                          class="p">,</span>
                        <span class="na">organization</span> <span class="p">=</span> <span class="s">{IEEE}</span><span
                          class="p">,</span>
                        <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">abbr</span> <span class="p">=</span> <span class="s">{ICME}</span><span
                          class="p">,</span>
                        <span class="na">code</span> <span class="p">=</span> <span
                          class="s">{https://github.com/jindongwang/transferlearning/tree/master/code/traditional/EasyTL}</span><span
                          class="p">,</span>
                        <span class="na">pdf</span> <span class="p">=</span> <span class="s">{a13_icme19.pdf}</span>
                        <span class="p">}</span></code></pre>
                  </figure>
                </div>
              </div>
              <!-- </div> -->
            </li>
            <li>
              <!-- <div class="row"> -->
              <!-- <div class="col-sm-1 abbr">
    <abbr class="badge">IJCNN</abbr>
  </div> -->
              <div
                id="yu2019accelerating"
                class="col-sm-8"
                style="max-width: 100%"
              >
                <div
                  class="title"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Accelerating deep unsupervised domain adaptation with transfer
                  channel pruning
                </div>
                <div
                  class="author"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Chaohui Yu,
                  <em><b>Jindong Wang</b></em>
                  , Yiqiang Chen, and Zijing Wu
                </div>
                <div
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: -0.1em;
                  "
                >
                  <em
                    >2019 International Joint Conference on Neural Networks
                    (IJCNN)</em
                  >
                  2019 | [
                  <a
                    href="#"
                    role="button"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >arXiv</a
                  >
                  <!-- 
        <a class="bibtex btn btn-sm z-depth-0" role="button" style="padding-top: 0em; padding-bottom: 0px;">Bib</a>
         -->
                  <a href="#" style="padding-top: 0em; padding-bottom: 0px"
                    >PDF</a
                  >
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >Code</a
                  >
                  ]
                </div>
                <!-- <div class="links" style="padding-top: 0em; padding-bottom: 0em; height: 30px; margin-bottom: -.5em;"> -->
                <!--  -->
                <!-- </div> -->
                <!-- Hidden abstract block -->
                <!-- Hidden bibtex block -->
                <div class="bibtex hidden">
                  <figure class="highlight">
                    <pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span
                          class="p">{</span><span class="nl">yu2019accelerating</span><span class="p">,</span>
                        <span class="na">title</span> <span class="p">=</span> <span class="s">{Accelerating deep
                          unsupervised domain adaptation with transfer channel pruning}</span><span class="p">,</span>
                        <span class="na">author</span> <span class="p">=</span> <span class="s">{Yu, Chaohui and Wang,
                          Jindong and Chen, Yiqiang and Wu, Zijing}</span><span class="p">,</span>
                        <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{2019 International
                          Joint Conference on Neural Networks (IJCNN)}</span><span class="p">,</span>
                        <span class="na">pages</span> <span class="p">=</span> <span class="s">{1--8}</span><span
                          class="p">,</span>
                        <span class="na">year</span> <span class="p">=</span> <span class="s">{2019}</span><span
                          class="p">,</span>
                        <span class="na">organization</span> <span class="p">=</span> <span class="s">{IEEE}</span><span
                          class="p">,</span>
                        <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">abbr</span> <span class="p">=</span> <span class="s">{IJCNN}</span><span
                          class="p">,</span>
                        <span class="na">arxiv</span> <span class="p">=</span> <span
                          class="s">{https://arxiv.org/pdf/1904.02654.pdf}</span><span class="p">,</span>
                        <span class="na">code</span> <span class="p">=</span> <span
                          class="s">{https://github.com/jindongwang/transferlearning/tree/master/code/deep/TCP}</span><span
                          class="p">,</span>
                        <span class="na">pdf</span> <span class="p">=</span> <span class="s">{ijcnn19-tcp.pdf}</span>
                        <span class="p">}</span></code></pre>
                  </figure>
                </div>
              </div>
              <!-- </div> -->
            </li>
          </ol>
          <div>2018</div>
          <ol class="bibliography">
            <li>
              <!-- <div class="row"> -->
              <!-- <div class="col-sm-1 abbr">
    <abbr class="badge">JMLC</abbr>
  </div> -->
              <div id="hu2018okrelm" class="col-sm-8" style="max-width: 100%">
                <div
                  class="title"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  OKRELM: online kernelized and regularized extreme learning
                  machine for wearable-based activity recognition
                </div>
                <div
                  class="author"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Lisha Hu, Yiqiang Chen,
                  <em><b>Jindong Wang</b></em>
                  , Chunyu Hu, and Xinlong Jiang
                </div>
                <div
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: -0.1em;
                  "
                >
                  <em
                    >International Journal of Machine Learning and Cybernetics
                    (JMLC)</em
                  >
                  2018 | [
                  <!-- 
        <a class="bibtex btn btn-sm z-depth-0" role="button" style="padding-top: 0em; padding-bottom: 0px;">Bib</a>
         -->
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >HTML</a
                  >
                  <a href="#" style="padding-top: 0em; padding-bottom: 0px"
                    >PDF</a
                  >
                  ]
                </div>
                <!-- <div class="links" style="padding-top: 0em; padding-bottom: 0em; height: 30px; margin-bottom: -.5em;"> -->
                <!--  -->
                <!-- </div> -->
                <!-- Hidden abstract block -->
                <!-- Hidden bibtex block -->
                <div class="bibtex hidden">
                  <figure class="highlight">
                    <pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span
                          class="p">{</span><span class="nl">hu2018okrelm</span><span class="p">,</span>
                        <span class="na">title</span> <span class="p">=</span> <span class="s">{OKRELM: online
                          kernelized and regularized extreme learning machine for wearable-based activity
                          recognition}</span><span class="p">,</span>
                        <span class="na">author</span> <span class="p">=</span> <span class="s">{Hu, Lisha and Chen,
                          Yiqiang and Wang, Jindong and Hu, Chunyu and Jiang, Xinlong}</span><span class="p">,</span>
                        <span class="na">journal</span> <span class="p">=</span> <span class="s">{International Journal
                          of Machine Learning and Cybernetics (JMLC)}</span><span class="p">,</span>
                        <span class="na">volume</span> <span class="p">=</span> <span class="s">{9}</span><span
                          class="p">,</span>
                        <span class="na">number</span> <span class="p">=</span> <span class="s">{9}</span><span
                          class="p">,</span>
                        <span class="na">pages</span> <span class="p">=</span> <span class="s">{1577--1590}</span><span
                          class="p">,</span>
                        <span class="na">year</span> <span class="p">=</span> <span class="s">{2018}</span><span
                          class="p">,</span>
                        <span class="na">publisher</span> <span class="p">=</span> <span
                          class="s">{Springer}</span><span class="p">,</span>
                        <span class="na">html</span> <span class="p">=</span> <span
                          class="s">{https://link.springer.com/article/10.1007/s13042-017-0666-8?wt_mc=Internal.Event.1.SEM.ArticleAuthorOnlineFirst#enumeration}</span><span
                          class="p">,</span>
                        <span class="na">pdf</span> <span class="p">=</span> <span class="s">{a06_ijmlc.pdf}</span><span
                          class="p">,</span>
                        <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">abbr</span> <span class="p">=</span> <span class="s">{JMLC}</span>
                        <span class="p">}</span></code></pre>
                  </figure>
                </div>
              </div>
              <!-- </div> -->
            </li>
            <li>
              <!-- <div class="row"> -->
              <!-- <div class="col-sm-1 abbr">
    <abbr class="badge">ACMMM</abbr>
  </div> -->
              <div id="wang2018visual" class="col-sm-8" style="max-width: 100%">
                <div
                  class="title"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Visual domain adaptation with manifold embedded distribution
                  alignment
                </div>
                <div
                  class="author"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  <em><b>Jindong Wang</b></em>
                  , Wenjie Feng, Yiqiang Chen, Han Yu, Meiyu Huang, and Philip S
                  Yu
                </div>
                <div
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: -0.1em;
                  "
                >
                  <em>The 26th ACM international conference on Multimedia</em>
                  2018 | [
                  <!-- 
        <a class="bibtex btn btn-sm z-depth-0" role="button" style="padding-top: 0em; padding-bottom: 0px;">Bib</a>
         -->
                  <a href="#" style="padding-top: 0em; padding-bottom: 0px"
                    >PDF</a
                  >
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >Supp</a
                  >
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >Code</a
                  >
                  <a href="#" style="padding-top: 0em; padding-bottom: 0px"
                    >Poster</a
                  >
                  ]
                </div>
                <div>
                  <b>(400+ citations; 2nd most cited paper in MM’18)</b>
                </div>
                <!-- <div class="links" style="padding-top: 0em; padding-bottom: 0em; height: 30px; margin-bottom: -.5em;"> -->
                <!--  -->
                <!-- </div> -->
                <!-- Hidden abstract block -->
                <!-- Hidden bibtex block -->
                <div class="bibtex hidden">
                  <figure class="highlight">
                    <pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span
                          class="p">{</span><span class="nl">wang2018visual</span><span class="p">,</span>
                        <span class="na">title</span> <span class="p">=</span> <span class="s">{Visual domain adaptation
                          with manifold embedded distribution alignment}</span><span class="p">,</span>
                        <span class="na">author</span> <span class="p">=</span> <span class="s">{Wang, Jindong and Feng,
                          Wenjie and Chen, Yiqiang and Yu, Han and Huang, Meiyu and Yu, Philip S}</span><span
                          class="p">,</span>
                        <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{The 26th ACM
                          international conference on Multimedia}</span><span class="p">,</span>
                        <span class="na">pages</span> <span class="p">=</span> <span class="s">{402--410}</span><span
                          class="p">,</span>
                        <span class="na">year</span> <span class="p">=</span> <span class="s">{2018}</span><span
                          class="p">,</span>
                        <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">abbr</span> <span class="p">=</span> <span class="s">{ACMMM}</span><span
                          class="p">,</span>
                        <span class="na">code</span> <span class="p">=</span> <span
                          class="s">{https://github.com/jindongwang/transferlearning/tree/master/code/traditional/MEDA}</span><span
                          class="p">,</span>
                        <span class="na">pdf</span> <span class="p">=</span> <span class="s">{a11_mm18.pdf}</span><span
                          class="p">,</span>
                        <span class="na">supp</span> <span class="p">=</span> <span
                          class="s">{https://www.jianguoyun.com/p/DRuWOFkQjKnsBRjkr2E}</span><span class="p">,</span>
                        <span class="na">poster</span> <span class="p">=</span> <span
                          class="s">{poster_mm18.pdf}</span><span class="p">,</span>
                        <span class="na">selected</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">special</span> <span class="p">=</span> <span class="s">{400+ citations; 2nd
                          most cited paper in MM'18}</span>
                        <span class="p">}</span></code></pre>
                  </figure>
                </div>
              </div>
              <!-- </div> -->
            </li>
            <li>
              <!-- <div class="row"> -->
              <!-- <div class="col-sm-1 abbr">
    <abbr class="badge">ICCSE</abbr>
  </div> -->
              <div id="wang2018deep" class="col-sm-8" style="max-width: 100%">
                <div
                  class="title"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Deep transfer learning for cross-domain activity recognition
                </div>
                <div
                  class="author"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  <em><b>Jindong Wang</b></em>
                  , Vincent W Zheng, Yiqiang Chen, and Meiyu Huang
                </div>
                <div
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: -0.1em;
                  "
                >
                  <em
                    >proceedings of the 3rd International Conference on Crowd
                    Science and Engineering</em
                  >
                  2018 | [
                  <!-- 
        <a class="bibtex btn btn-sm z-depth-0" role="button" style="padding-top: 0em; padding-bottom: 0px;">Bib</a>
         -->
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >HTML</a
                  >
                  <a href="#" style="padding-top: 0em; padding-bottom: 0px"
                    >PDF</a
                  >
                  ]
                </div>
                <!-- <div class="links" style="padding-top: 0em; padding-bottom: 0em; height: 30px; margin-bottom: -.5em;"> -->
                <!--  -->
                <!-- </div> -->
                <!-- Hidden abstract block -->
                <!-- Hidden bibtex block -->
                <div class="bibtex hidden">
                  <figure class="highlight">
                    <pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span
                          class="p">{</span><span class="nl">wang2018deep</span><span class="p">,</span>
                        <span class="na">title</span> <span class="p">=</span> <span class="s">{Deep transfer learning
                          for cross-domain activity recognition}</span><span class="p">,</span>
                        <span class="na">author</span> <span class="p">=</span> <span class="s">{Wang, Jindong and
                          Zheng, Vincent W and Chen, Yiqiang and Huang, Meiyu}</span><span class="p">,</span>
                        <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{proceedings of the
                          3rd International Conference on Crowd Science and Engineering}</span><span class="p">,</span>
                        <span class="na">pages</span> <span class="p">=</span> <span class="s">{1--8}</span><span
                          class="p">,</span>
                        <span class="na">year</span> <span class="p">=</span> <span class="s">{2018}</span><span
                          class="p">,</span>
                        <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">abbr</span> <span class="p">=</span> <span class="s">{ICCSE}</span><span
                          class="p">,</span>
                        <span class="na">pdf</span> <span class="p">=</span> <span
                          class="s">{a12_iccse18.pdf}</span><span class="p">,</span>
                        <span class="na">html</span> <span class="p">=</span> <span
                          class="s">{https://dl.acm.org/citation.cfm?id=3265705}</span>
                        <span class="p">}</span></code></pre>
                  </figure>
                </div>
              </div>
              <!-- </div> -->
            </li>
            <li>
              <!-- <div class="row"> -->
              <!-- <div class="col-sm-1 abbr">
    <abbr class="badge">PerCom</abbr>
  </div> -->
              <div
                id="wang2018stratified"
                class="col-sm-8"
                style="max-width: 100%"
              >
                <div
                  class="title"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Stratified transfer learning for cross-domain activity
                  recognition
                </div>
                <div
                  class="author"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  <em><b>Jindong Wang</b></em>
                  , Yiqiang Chen, Lisha Hu, Xiaohui Peng, and S Yu Philip
                </div>
                <div
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: -0.1em;
                  "
                >
                  <em
                    >IEEE international conference on pervasive computing and
                    communications (PerCom)</em
                  >
                  2018 | [
                  <!-- 
        <a class="bibtex btn btn-sm z-depth-0" role="button" style="padding-top: 0em; padding-bottom: 0px;">Bib</a>
         -->
                  <a href="#" style="padding-top: 0em; padding-bottom: 0px"
                    >PDF</a
                  >
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >Code</a
                  >
                  ]
                </div>
                <!-- <div class="links" style="padding-top: 0em; padding-bottom: 0em; height: 30px; margin-bottom: -.5em;"> -->
                <!--  -->
                <!-- </div> -->
                <!-- Hidden abstract block -->
                <!-- Hidden bibtex block -->
                <div class="bibtex hidden">
                  <figure class="highlight">
                    <pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span
                          class="p">{</span><span class="nl">wang2018stratified</span><span class="p">,</span>
                        <span class="na">title</span> <span class="p">=</span> <span class="s">{Stratified transfer
                          learning for cross-domain activity recognition}</span><span class="p">,</span>
                        <span class="na">author</span> <span class="p">=</span> <span class="s">{Wang, Jindong and Chen,
                          Yiqiang and Hu, Lisha and Peng, Xiaohui and Philip, S Yu}</span><span class="p">,</span>
                        <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{IEEE international
                          conference on pervasive computing and communications (PerCom)}</span><span class="p">,</span>
                        <span class="na">pages</span> <span class="p">=</span> <span class="s">{1--10}</span><span
                          class="p">,</span>
                        <span class="na">year</span> <span class="p">=</span> <span class="s">{2018}</span><span
                          class="p">,</span>
                        <span class="na">organization</span> <span class="p">=</span> <span class="s">{IEEE}</span><span
                          class="p">,</span>
                        <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">abbr</span> <span class="p">=</span> <span class="s">{PerCom}</span><span
                          class="p">,</span>
                        <span class="na">code</span> <span class="p">=</span> <span
                          class="s">{https://github.com/jindongwang/activityrecognition/tree/master/code/percom18_stl}</span><span
                          class="p">,</span>
                        <span class="na">pdf</span> <span class="p">=</span> <span class="s">{a09_percom18.pdf}</span>
                        <span class="p">}</span></code></pre>
                  </figure>
                </div>
              </div>
              <!-- </div> -->
            </li>
          </ol>
          <div>2017</div>
          <ol class="bibliography">
            <li>
              <!-- <div class="row"> -->
              <!-- <div class="col-sm-1 abbr">
    <abbr class="badge">ICDM</abbr>
  </div> -->
              <div
                id="wang2017balanced"
                class="col-sm-8"
                style="max-width: 100%"
              >
                <div
                  class="title"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Balanced distribution adaptation for transfer learning
                </div>
                <div
                  class="author"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  <em><b>Jindong Wang</b></em>
                  , Yiqiang Chen, Shuji Hao, Wenjie Feng, and Zhiqi Shen
                </div>
                <div
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: -0.1em;
                  "
                >
                  <em>IEEE international conference on data mining (ICDM)</em>
                  2017 | [
                  <!-- 
        <a class="bibtex btn btn-sm z-depth-0" role="button" style="padding-top: 0em; padding-bottom: 0px;">Bib</a>
         -->
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >HTML</a
                  >
                  <a href="#" style="padding-top: 0em; padding-bottom: 0px"
                    >PDF</a
                  >
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >Code</a
                  >
                  ]
                </div>
                <div><b>(400+ citations; most cited paper in ICDM’17)</b></div>
                <!-- <div class="links" style="padding-top: 0em; padding-bottom: 0em; height: 30px; margin-bottom: -.5em;"> -->
                <!--  -->
                <!-- </div> -->
                <!-- Hidden abstract block -->
                <!-- Hidden bibtex block -->
                <div class="bibtex hidden">
                  <figure class="highlight">
                    <pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span
                          class="p">{</span><span class="nl">wang2017balanced</span><span class="p">,</span>
                        <span class="na">title</span> <span class="p">=</span> <span class="s">{Balanced distribution
                          adaptation for transfer learning}</span><span class="p">,</span>
                        <span class="na">author</span> <span class="p">=</span> <span class="s">{Wang, Jindong and Chen,
                          Yiqiang and Hao, Shuji and Feng, Wenjie and Shen, Zhiqi}</span><span class="p">,</span>
                        <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{IEEE international
                          conference on data mining (ICDM)}</span><span class="p">,</span>
                        <span class="na">pages</span> <span class="p">=</span> <span class="s">{1129--1134}</span><span
                          class="p">,</span>
                        <span class="na">year</span> <span class="p">=</span> <span class="s">{2017}</span><span
                          class="p">,</span>
                        <span class="na">organization</span> <span class="p">=</span> <span class="s">{IEEE}</span><span
                          class="p">,</span>
                        <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">abbr</span> <span class="p">=</span> <span class="s">{ICDM}</span><span
                          class="p">,</span>
                        <span class="na">code</span> <span class="p">=</span> <span
                          class="s">{https://github.com/jindongwang/transferlearning/tree/master/code/BDA}</span><span
                          class="p">,</span>
                        <span class="na">pdf</span> <span class="p">=</span> <span
                          class="s">{a08_icdm17.pdf}</span><span class="p">,</span>
                        <span class="na">html</span> <span class="p">=</span> <span
                          class="s">{http://ieeexplore.ieee.org/document/8215613/?part=1}</span><span class="p">,</span>
                        <span class="na">selected</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">special</span> <span class="p">=</span> <span class="s">{400+ citations; most
                          cited paper in ICDM'17}</span>
                        <span class="p">}</span></code></pre>
                  </figure>
                </div>
              </div>
              <!-- </div> -->
            </li>
          </ol>
          <div>2016</div>
          <ol class="bibliography">
            <li>
              <!-- <div class="row"> -->
              <!-- <div class="col-sm-1 abbr">
    <abbr class="badge">UbiComp</abbr>
  </div> -->
              <div id="chen2016ocean" class="col-sm-8" style="max-width: 100%">
                <div
                  class="title"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Ocean: A new opportunistic computing model for wearable
                  activity recognition
                </div>
                <div
                  class="author"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Yiqiang Chen, Yang Gu, Xinlong Jiang, and
                  <em><b>Jindong Wang#</b></em>
                </div>
                <div
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: -0.1em;
                  "
                >
                  <em
                    >Proceedings of the 2016 ACM International Joint Conference
                    on Pervasive and Ubiquitous Computing: Adjunct</em
                  >
                  2016 | [
                  <!-- 
        <a class="bibtex btn btn-sm z-depth-0" role="button" style="padding-top: 0em; padding-bottom: 0px;">Bib</a>
         -->
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >HTML</a
                  >
                  <a href="#" style="padding-top: 0em; padding-bottom: 0px"
                    >PDF</a
                  >
                  ]
                </div>
                <!-- <div class="links" style="padding-top: 0em; padding-bottom: 0em; height: 30px; margin-bottom: -.5em;"> -->
                <!--  -->
                <!-- </div> -->
                <!-- Hidden abstract block -->
                <!-- Hidden bibtex block -->
                <div class="bibtex hidden">
                  <figure class="highlight">
                    <pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span
                          class="p">{</span><span class="nl">chen2016ocean</span><span class="p">,</span>
                        <span class="na">title</span> <span class="p">=</span> <span class="s">{Ocean: A new
                          opportunistic computing model for wearable activity recognition}</span><span
                          class="p">,</span>
                        <span class="na">author</span> <span class="p">=</span> <span class="s">{Chen, Yiqiang and Gu,
                          Yang and Jiang, Xinlong and Wang, Jindong}</span><span class="p">,</span>
                        <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{Proceedings of the
                          2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing:
                          Adjunct}</span><span class="p">,</span>
                        <span class="na">pages</span> <span class="p">=</span> <span class="s">{33--36}</span><span
                          class="p">,</span>
                        <span class="na">year</span> <span class="p">=</span> <span class="s">{2016}</span><span
                          class="p">,</span>
                        <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">abbr</span> <span class="p">=</span> <span class="s">{UbiComp}</span><span
                          class="p">,</span>
                        <span class="na">pdf</span> <span class="p">=</span> <span
                          class="s">{a04_ubicomp16.pdf}</span><span class="p">,</span>
                        <span class="na">html</span> <span class="p">=</span> <span
                          class="s">{http://dl.acm.org/citation.cfm?id=2971453}</span>
                        <span class="p">}</span></code></pre>
                  </figure>
                </div>
              </div>
              <!-- </div> -->
            </li>
            <li>
              <div id="hu2016less" class="col-sm-8" style="max-width: 100%">
                <div
                  class="title"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Less annotation on personalized activity recognition using
                  context data
                </div>
                <div
                  class="author"
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: 0em;
                  "
                >
                  Lisha Hu, Yiqiang Chen, Shuangquan Wang,
                  <em><b>Jindong Wang</b></em>
                  , Jianfei Shen, Xinlong Jiang, and Zhiqi Shen
                </div>
                <div
                  style="
                    padding-top: 0em;
                    padding-bottom: 0px;
                    margin-bottom: -0.1em;
                  "
                >
                  <em
                    >2016 Intl IEEE Conferences on Ubiquitous Intelligence
                    Computing (UIC)</em
                  >
                  2016 | [
                  <!-- 
        <a class="bibtex btn btn-sm z-depth-0" role="button" style="padding-top: 0em; padding-bottom: 0px;">Bib</a>
         -->
                  <a
                    href="#"
                    style="padding-top: 0em; padding-bottom: 0px"
                    target="_blank"
                    rel="noopener noreferrer"
                    >HTML</a
                  >
                  <a href="#" style="padding-top: 0em; padding-bottom: 0px"
                    >PDF</a
                  >
                  ]
                </div>
                <!-- <div class="links" style="padding-top: 0em; padding-bottom: 0em; height: 30px; margin-bottom: -.5em;"> -->
                <!--  -->
                <!-- </div> -->
                <!-- Hidden abstract block -->
                <!-- Hidden bibtex block -->
                <div class="bibtex hidden">
                  <figure class="highlight">
                    <pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span
                          class="p">{</span><span class="nl">hu2016less</span><span class="p">,</span>
                        <span class="na">title</span> <span class="p">=</span> <span class="s">{Less annotation on
                          personalized activity recognition using context data}</span><span class="p">,</span>
                        <span class="na">author</span> <span class="p">=</span> <span class="s">{Hu, Lisha and Chen,
                          Yiqiang and Wang, Shuangquan and Wang, Jindong and Shen, Jianfei and Jiang, Xinlong and Shen,
                          Zhiqi}</span><span class="p">,</span>
                        <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{2016 Intl IEEE
                          Conferences on Ubiquitous Intelligence Computing (UIC)}</span><span class="p">,</span>
                        <span class="na">pages</span> <span class="p">=</span> <span class="s">{327--332}</span><span
                          class="p">,</span>
                        <span class="na">year</span> <span class="p">=</span> <span class="s">{2016}</span><span
                          class="p">,</span>
                        <span class="na">organization</span> <span class="p">=</span> <span class="s">{IEEE}</span><span
                          class="p">,</span>
                        <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span
                          class="p">,</span>
                        <span class="na">abbr</span> <span class="p">=</span> <span class="s">{UIC}</span><span
                          class="p">,</span>
                        <span class="na">pdf</span> <span class="p">=</span> <span class="s">{a02_uic16.pdf}</span><span
                          class="p">,</span>
                        <span class="na">html</span> <span class="p">=</span> <span
                          class="s">{http://ieeexplore.ieee.org/abstract/document/7816862/}</span>
                        <span class="p">}</span></code></pre>
                  </figure>
                </div>
              </div>
              <!-- </div> -->
            </li>
          </ol>
        </div>
      </article>
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